Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space


Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks


Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks


Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels


Predicting Merge Conflicts in Collaborative Software Development


Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory


Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning


Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition


Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network


Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes


Predicting Music Hierarchies with a Graph-Based Neural Decoder


Predicting Music Relistening Behavior Using the ACT-R Framework


Predicting Next Local Appearance for Video Anomaly Detection


Predicting Novel Functional Roles of Designed Small Biomolecules: An ML Approach Utilizing PubChem Compound and Substance Identifiers (CID-SID ML model)


Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks


Predicting Open-Hole Laminates Failure Using Support Vector Machines With Classical and Quantum Kernels


Predicting Opinion Dynamics via Sociologically-Informed Neural Networks


Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network


Predicting Outcomes in Video Games with Long Short Term Memory Networks


Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning


Predicting Pairwise Relations with Neural Similarity Encoders


Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach


Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander


Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study


Predicting Patch Correctness Based on the Similarity of Failing Test Cases


Predicting Patient Outcomes with Graph Representation Learning


Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learning


Predicting Performance for Natural Language Processing Tasks


Predicting Performance of Heterogeneous AI Systems with Discrete-Event Simulations


Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests


Predicting Physical World Destinations for Commands Given to Self-Driving Cars


Predicting Plasma Temperature From Line Intensities Using ML Models


Predicting Political Ideology from Digital Footprints


Predicting Positive and Negative Links with Noisy Queries: Theory & Practice


Predicting Postoperative Nausea And Vomiting Using Machine Learning: A Model Development and Validation Study


Predicting Practically? Domain Generalization for Predictive Analytics in Real-world Environments


Predicting Privacy Preferences for Smart Devices as Norms


Predicting Progression Events in Multiple Myeloma from Routine Blood Work


Predicting Properties of Nodes via Community-Aware Features


Predicting RSO Populations Using a Neighbouring Orbits Technique


Predicting Rare Events by Shrinking Towards Proportional Odds


Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning


Predicting Relationship Labels and Individual Personality Traits from Telecommunication History in Social Networks using Hawkes Processes


Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks


Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics


Predicting Routine Object Usage for Proactive Robot Assistance


Predicting SMT Solver Performance for Software Verification


Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification


Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement


Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks


Predicting Semantic Relations using Global Graph Properties


Predicting Semen Motility using three-dimensional Convolutional Neural Networks


Predicting Sentence-Level Factuality of News and Bias of Media Outlets


Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches


Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields


Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13


Predicting Software Performance with Divide-and-Learn


Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data


Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via Shifted Operator Inference


Predicting Solid State Material Platforms for Quantum Technologies


Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment


Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model


Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing


Predicting Strategic Behavior from Free Text


Predicting Strategic Energy Storage Behaviors


Predicting Stroke from Electronic Health Records


Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning


Predicting Subjective Features of Questions of QA Websites using BERT


Predicting Surface Texture in Steel Manufacturing at Speed


Predicting Surgical Safety Margins in Osteosarcoma Knee Resections: An Unsupervised Approach


Predicting Survival Time of Ball Bearings in the Presence of Censoring


Predicting Survival of Tongue Cancer Patients by Machine Learning Models


Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments


Predicting Temporal Sets with Deep Neural Networks


Predicting Text Readability from Scrolling Interactions


Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark


Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning


Predicting Time Series of Networked Dynamical Systems without Knowing Topology


Predicting Transcription Factor Binding Sites using Transformer based Capsule Network


Predicting Turn-Taking and Backchannel in Human-Machine Conversations Using Linguistic, Acoustic, and Visual Signals


Predicting Tweet Engagement with Graph Neural Networks


Predicting Twitter User Socioeconomic Attributes with Network and Language Information


Predicting User Emotional Tone in Mental Disorder Online Communities


Predicting User Intents and Musical Attributes from Music Discovery Conversations


Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance


Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer


Predicting Visual Attention and Distraction During Visual Search Using Convolutional Neural Networks


Predicting Visual Features from Text for Image and Video Caption Retrieval


Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings


Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning


Predicting Winners of the Reality TV Dating Show $\textit{The Bachelor}$ Using Machine Learning Algorithms


Predicting Workload in Virtual Flight Simulations using EEG Features (Including Post-hoc Analysis in Appendix)


Predicting Yelp Star Reviews Based on Network Structure with Deep Learning


Predicting a Protein's Stability under a Million Mutations


Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation


Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks


Predicting and Understanding College Student Mental Health with Interpretable Machine Learning


Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI


Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning


Predicting aqueous and electrochemical stability of 2D materials from extended Pourbaix analyses


Predicting biosignatures for nutrient limited biospheres


Predicting blood pressure under circumstances of missing data: An analysis of missing data patterns and imputation methods using NHANES


Predicting bulge to total luminosity ratio of galaxies using deep learning


Predicting butterfly species presence from satellite imagery using soft contrastive regularisation


Predicting cell phone adoption metrics using satellite imagery


Predicting cognitive scores with graph neural networks through sample selection learning


Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering


Predicting consumers engagement on Facebook based on what and how companies write


Predicting crop yields with little ground truth: A simple statistical model for in-season forecasting


Predicting delays in Indian lower courts using AutoML and Decision Forests


Predicting discrete-time bifurcations with deep learning


Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining


Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses


Predicting erectile dysfunction after treatment for localized prostate cancer


Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology


Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge


Predicting function of evolutionarily implausible DNA sequences


Predicting future astronomical events using deep learning


Predicting galaxy spectra from images with hybrid convolutional neural networks


Predicting household socioeconomic position in Mozambique using satellite and household imagery


Predicting human decision making in psychological tasks with recurrent neural networks


Predicting interstellar radiation fields from chemical evolution models


Predicting into unknown space? Estimating the area of applicability of spatial prediction models


Predicting intubation support requirement of patients using Chest X-ray with Deep Representation Learning


Predicting ionic conductivity in solids from the machine-learned potential energy landscape


Predicting kills in Game of Thrones using network properties


Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles


Predicting mosquito flight behavior using Bayesian dynamical systems learning


Predicting multiple sclerosis disease severity with multimodal deep neural networks


Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model


Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning


Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network


Predicting pairwise preferences between TTS audio stimuli using parallel ratings data and anti-symmetric twin neural networks


Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction


Predicting parameters for the Quantum Approximate Optimization Algorithm for MAX-CUT from the infinite-size limit


Predicting performance difficulty from piano sheet music images


Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression


Predicting polymerization reactions via transfer learning using chemical language models


Predicting population extinction in lattice-based birth-death-movement models


Predicting pregnancy using large-scale data from a women's health tracking mobile application


Predicting protein variants with equivariant graph neural networks


Predicting ptychography probe positions using single-shot phase retrieval neural network


Predicting radial-velocity jitter induced by stellar oscillations based on Kepler data


Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling


Predicting rate kernels via dynamic mode decomposition


Predicting readmission risk from doctors' notes


Predicting resolved galaxy properties from photometric images using convolutional neural networks


Predicting risk of cardiovascular disease using retinal OCT imaging


Predicting seasonal influenza using supermarket retail records


Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach


Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks


Predicting solvation free energies with an implicit solvent machine learning potential


Predicting students' performance in online courses using multiple data sources


Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria


Predicting temperatures in Brazilian states capitals via Machine Learning


Predicting the Accuracy of Early-est Earthquake Magnitude Estimates with an LSTM Neural Network: A Preliminary Analysis


Predicting the Best of N Visual Trackers


Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models


Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks


Predicting the Computational Cost of Deep Learning Models


Predicting the Driver's Focus of Attention: the DR(eye)VE Project


Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties


Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations


Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning


Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC


Predicting the Geolocation of Tweets Using transformer models on Customized Data


Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning


Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information


Predicting the Next Best View for 3D Mesh Refinement


Predicting the Number of Reported Bugs in a Software Repository


Predicting the Objective and Priority of Issue Reports in Software Repositories


Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio


Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning


Predicting the Performance of Black-box LLMs through Self-Queries


Predicting the Physical Dynamics of Unseen 3D Objects


Predicting the Politics of an Image Using Webly Supervised Data


Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework


Predicting the Quality of Revisions in Argumentative Writing


Predicting the Role of Political Trolls in Social Media


Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network


Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks


Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks


Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks


Predicting the Temporal Dynamics of Prosthetic Vision


Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks


Predicting the Understandability of Computational Notebooks through Code Metrics Analysis


Predicting the binding of small molecules to proteins through invariant representation of the molecular structure


Predicting the cardinality and maximum degree of a reduced Gröbner basis


Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods


Predicting the emergence of localised dihedral patterns in models for dryland vegetation


Predicting the fatigue life of asphalt concrete using neural networks


Predicting the generalization gap in neural networks using topological data analysis


Predicting the genetic component of gene expression using gene regulatory networks


Predicting the global far-infrared SED of galaxies via machine learning techniques


Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning


Predicting the impact of formation protocols on battery lifetime immediately after manufacturing


Predicting the impact of treatments over time with uncertainty aware neural differential equations


Predicting the phase diagram of titanium dioxide with random search and pattern recognition


Predicting the popularity of information on social platforms without underlying network structure


Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes


Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network


Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data


Predicting wide binaries and deviations from standard gravity using machine learning algorithms


Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained models


Prediction Error Estimation in Random Forests


Prediction Error-based Classification for Class-Incremental Learning


Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment


Prediction Focused Topic Models via Feature Selection


Prediction Intervals for Synthetic Control Methods


Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles


Prediction Models That Learn to Avoid Missing Values


Prediction Sets Adaptive to Unknown Covariate Shift


Prediction Sets and Conformal Inference with Interval Outcomes


Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving


Prediction and Control in Continual Reinforcement Learning


Prediction and Prevention of Pandemics via Graphical Model Inference and Convex Programming


Prediction and control of spatiotemporal chaos by learning conjugate tubular neighborhoods


Prediction and mitigation of nonlocal cascading failures using graph neural networks


Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study


Prediction intervals for economic fixed-event forecasts


Prediction intervals for random-effects meta-analysis: a confidence distribution approach


Prediction modelling with many correlated and zero-inflated predictors: assessing a nonnegative garrote approach


Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings


Prediction of Astrometric-Microlensing Events from Gaia eDR3 Proper Motions


Prediction of Cellular Identities from Trajectory and Cell Fate Information


Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks


Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions


Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation


Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network


Prediction of Hereditary Cancers Using Neural Networks


Prediction of Homicides in Urban Centers: A Machine Learning Approach


Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil


Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone


Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database


Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier


Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning


Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory


Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees


Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model


Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study


Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables


Prediction of defect properties in concentrated solid solutions using a Langmuir-like model


Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms


Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness -- a data driven approach


Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet


Prediction of microstructural representativity from a single image


Prediction of soft proton intensities in the near-Earth space using machine learning


Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy


Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy


Prediction of the neutron drip line in oxygen isotopes using quantum computation


Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification


Prediction rigidities for data-driven chemistry


Prediction techniques for dynamic imaging with online primal-dual methods


Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation


Prediction uncertainty validation for computational chemists


Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data


Prediction, Communication, and Computing Duration Optimization for VR Video Streaming


Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations


Prediction-Centric Uncertainty Quantification via MMD


Prediction-Correction Pedestrian Flow by Means of Minimum Flow Problem


Prediction-Guided Distillation for Dense Object Detection


Prediction-Powered Adaptive Shrinkage Estimation


Prediction-Powered Ranking of Large Language Models


Prediction-based Resource Allocation using Bayesian Neural Networks and Minimum Cost and Maximum Flow Algorithm


Prediction-powered Generalization of Causal Inferences


Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning


Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI


Predictions for Electromagnetic Counterparts to Neutron Star Mergers Discovered during LIGO-Virgo-KAGRA Observing Runs 4 and 5


Predictions for Neutrinos and New Physics from Forward Heavy Hadron Production at the LHC


Predictions of planet detections with near infrared radial velocities in the up-coming SPIRou Legacy Survey-Planet Search


Predictive Accuracy-Based Active Learning for Medical Image Segmentation


Predictive Analytics of Varieties of Potatoes


Predictive Authoring for Brazilian Portuguese Augmentative and Alternative Communication


Predictive Auto-scaling with OpenStack Monasca


Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction


Predictive Chemistry Augmented with Text Retrieval


Predictive Coding Approximates Backprop along Arbitrary Computation Graphs


Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction


Predictive Coding for Decision Transformer


Predictive Coding for Locally-Linear Control


Predictive Data Selection: The Data That Predicts Is the Data That Teaches


Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology


Predictive Experience Replay for Continual Visual Control and Forecasting


Predictive Flows for Faster Ford-Fulkerson


Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling


Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse


Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation


Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding


Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM


Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning


Predictive Modeling of Anatomy with Genetic and Clinical Data


Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach


Predictive Modeling with Temporal Graphical Representation on Electronic Health Records


Predictive Multiplicity in Classification


Predictive Performance Comparison of Decision Policies Under Confounding


Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty


Predictive Querying for Autoregressive Neural Sequence Models


Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models


Predictive Sampling for Efficient Pairwise Subjective Image Quality Assessment


Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo


Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction


Predictive Synthesis of Control Barrier Functions and its Application to Time-Varying Constraints


Predictive Uncertainty-based Bias Mitigation in Ranking


Predictive Visual Tracking: A New Benchmark and Baseline Approach


Predictive World Models from Real-World Partial Observations


Predictive analysis of Bitcoin price considering social sentiments


Predictive and Generative Neural Networks for Object Functionality


Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management


Predictive inference for travel time on transportation networks


Predictive online optimisation with applications to optical flow


Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data


Predictive power of a Bayesian effective action for fully-connected one hidden layer neural networks in the proportional limit


Predictive process mining by network of classifiers and clusterers: the PEDF model


Predictive safety filter using system level synthesis


Predictive, scalable and interpretable knowledge tracing on structured domains


Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition


Predictor-corrector algorithms for stochastic optimization under gradual distribution shift


Predictors of Well-being and Productivity among Software Professionals during the COVID-19 Pandemic -- A Longitudinal Study


Predominant Musical Instrument Classification based on Spectral Features


Preemptive Holistic Collaborative System and Its Application in Road Transportation


Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks


PrefGen: Preference Guided Image Generation with Relative Attributes


Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning


Preference Communication in Multi-Objective Normal-Form Games


Preference Distillation for Personalized Generative Recommendation


Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes


Preference Leakage: A Contamination Problem in LLM-as-a-judge


Preference Learning with Lie Detectors can Induce Honesty or Evasion


Preference Modeling with Context-Dependent Salient Features


Preference Optimization for Molecular Language Models


Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models


Preference Ranking Optimization for Human Alignment


Preference Transformer: Modeling Human Preferences using Transformers for RL


Preference Tuning For Toxicity Mitigation Generalizes Across Languages


Preference-Aware Delivery Planning for Last-Mile Logistics


Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots


Preference-Guided Reflective Sampling for Aligning Language Models


Preference-Guided Reinforcement Learning for Efficient Exploration


Preference-Optimized Pareto Set Learning for Blackbox Optimization


Preference-based Interactive Multi-Document Summarisation


Preference-based Learning of Reward Function Features


Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering


PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning


Preferences Implicit in the State of the World


Preferences Prediction using a Gallery of Mobile Device based on Scene Recognition and Object Detection


Preferential Subsampling for Stochastic Gradient Langevin Dynamics


Preferential Temporal Difference Learning


Prefill-Based Jailbreak: A Novel Approach of Bypassing LLM Safety Boundary


Prefix Guidance: A Steering Wheel for Large Language Models to Defend Against Jailbreak Attacks


Prefix Propagation: Parameter-Efficient Tuning for Long Sequences


Prefix tuning for automated audio captioning


Prefix-Free Parsing for Building Big BWTs


Prefix-free parsing for building large tunnelled Wheeler graphs


PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation


PrefixLLM: LLM-aided Prefix Circuit Design


Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting


Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning


Preliminaries of a Space Situational Awareness Ontology


Preliminary Guidelines For Combining Data Integration and Visual Data Analysis


Preliminary Investigation into Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving


Preliminary Results of a Scientometric Analysis of the German Information Retrieval Community 2020-2023


Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems


Preliminary WMT24 Ranking of General MT Systems and LLMs


Preliminary report: Initial evaluation of StdPar implementations on AMD GPUs for HPC


Preliminary results from 5 years' spectral monitoring of Antares


Preliminary results on the herdability of complex multiagent systems via local information


Premise Selection for Theorem Proving by Deep Graph Embedding


Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning


Preordering: A hybrid of correlation clustering and partial ordering


Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models


Prepare-and-measure scenarios with photon-number constraints


Preparing Ground and Excited States Using Adiabatic CoVaR


Preparing Lessons for Progressive Training on Language Models


Preparing for Gaia Searches for Optical Counterparts of Gravitational Wave Events during O4


Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos


Preparing for low surface brightness science with the Vera C. Rubin Observatory: A Comparison of Observable and Simulated Intracluster Light Fractions


Preparing thermal states on noiseless and noisy programmable quantum processors


Preparing to discover the unknown with Rubin LSST -- I: Time domain


PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing


PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints using Literature Databases


Preprocessing Reward Functions for Interpretability


Preprocessing is All You Need: Boosting the Performance of Log Parsers With a General Preprocessing Framework


Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems


Prerequisite Structure Discovery in Intelligent Tutoring Systems


Prescribed Robustness in Optimal Power Flow


Prescribed-time Control for Perturbed Euler-Lagrange Systems with Obstacle Avoidance


Prescriptive Business Process Monitoring for Recommending Next Best Actions


Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach


Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach


Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction


Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration


Presence or Absence: Are Unknown Word Usages in Dictionaries?


Present and Future Generalization of Synthetic Image Detectors


PresentAgent: Multimodal Agent for Presentation Video Generation


Presentations for cusped arithmetic hyperbolic lattices


Presentations for the groups of order $p^6$ for prime $p\geq 7$


Presenting Unbinned Differential Cross Section Results


Presenting a Larger Up-to-date Movie Dataset and Investigating the Effects of Pre-released Attributes on Gross Revenue


Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification


Preservation of the Global Knowledge by Not-True Distillation in Federated Learning


Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts


Preserved central model for faster bidirectional compression in distributed settings


Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers


Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems


Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction


Preserving Fine-Grain Feature Information in Classification via Entropic Regularization


Preserving Full Degradation Details for Blind Image Super-Resolution


Preserving Generalization of Language models in Few-shot Continual Relation Extraction


Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction


Preserving Linear Separability in Continual Learning by Backward Feature Projection


Preserving Locality in Vision Transformers for Class Incremental Learning


Preserving Modality Structure Improves Multi-Modal Learning


Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality


Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning


Preserving Power Optimizations Across the High Level Synthesis of Distinct Application-Specific Circuits


Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models


Preserving Privacy in Cloud-based Data-Driven Stabilization


Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions


Preserving Relative Localization of FoV-Limited Drone Swarm via Active Mutual Observation


Preserving Semantics in Textual Adversarial Attacks


Preserving Speaker Information in Direct Speech-to-Speech Translation with Non-Autoregressive Generation and Pretraining


Preserving Tumor Volumes for Unsupervised Medical Image Registration


Preserving linear invariants in ensemble filtering methods


Preserving logical and functional dependencies in synthetic tabular data


Preserving physically important variables in optimal event selections: A case study in Higgs physics


Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy


Preserving the Ephemeral: Instagram Story Archiving with the Tidal Tales Plugin


Preserving the Privacy of Reward Functions in MDPs through Deception


Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging


Pressure Test: Quantifying the impact of positive stress on companies from online employee reviews


Pressure control using stochastic cell rescaling


Pressure-induced Shape-shifting of Helical Bacteria


Pressure-induced nearly perfect rectangular lattice and superconductivity in an organic molecular crystal (DMET-TTF)$_2$AuBr$_2$


PressureVision: Estimating Hand Pressure from a Single RGB Image


Prestige drives epistemic inequality in the diffusion of scientific ideas


Presumed Cultural Identity: How Names Shape LLM Responses


Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis


Pretext Tasks selection for multitask self-supervised speech representation learning


Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning


Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval


Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-physical Systems


Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning


Pretrained Cost Model for Distributed Constraint Optimization Problems


Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain


Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification


Pretrained Image-Text Models are Secretly Video Captioners


Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading


Pretrained Language Model Embryology: The Birth of ALBERT


Pretrained Language Models are Symbolic Mathematics Solvers too!


Pretrained Language Models as Visual Planners for Human Assistance


Pretrained Language Models for Dialogue Generation with Multiple Input Sources


Pretrained Language Models for Sequential Sentence Classification


Pretrained Models for Multilingual Federated Learning


Pretrained Optimization Model for Zero-Shot Black Box Optimization


Pretrained Transformers Improve Out-of-Distribution Robustness


Pretrained Transformers as Universal Computation Engines


Pretrained Transformers for Offensive Language Identification in Tanglish


Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage


Pretrained audio neural networks for Speech emotion recognition in Portuguese


Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis


Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method


Pretraining Deformable Image Registration Networks with Random Images


Pretraining Federated Text Models for Next Word Prediction


Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design


Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling


Pretraining Language Models to Ponder in Continuous Space


Pretraining Language Models with Text-Attributed Heterogeneous Graphs


Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning


Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning


Pretraining Representations for Data-Efficient Reinforcement Learning


Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning


Pretraining Strategy for Neural Potentials


Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators


Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-rays


Pretraining a Neural Operator in Lower Dimensions


Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets


Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification


Pretraining on Interactions for Learning Grounded Affordance Representations


Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression


Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning


Pretraining with Random Noise for Fast and Robust Learning without Weight Transport


Pretraining with random noise for uncertainty calibration


Pretty Good Strategies and Where to Find Them


Pretty darn good control: when are approximate solutions better than approximate models


PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction


Prevalence Estimation from Random Samples and Census Data with Participation Bias


Prevalence and scalable control of localized networks


Prevalence of two-dimensional photonic topology


Prevalence, Contents and Automatic Detection of KL-SATD


Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data


Prevent the Language Model from being Overconfident in Neural Machine Translation


Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks


Preventing Author Profiling through Zero-Shot Multilingual Back-Translation


Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection


Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective


Preventing Clean Label Poisoning using Gaussian Mixture Loss


Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework


Preventing Extreme Polarization of Political Attitudes


Preventing Inferences through Data Dependencies on Sensitive Data


Preventing Language Models From Hiding Their Reasoning


Preventing Local Pitfalls in Vector Quantization via Optimal Transport


Preventing Manifold Intrusion with Locality: Local Mixup


Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism


Preventing Model Collapse in Gaussian Process Latent Variable Models


Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database


Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph


Preventing Rogue Agents Improves Multi-Agent Collaboration


Preventing SARS-CoV-2 superspreading events with antiviral intranasal sprays


Preventing Unconstrained CBF Safety Filters Caused by Invalid Relative Degree Assumptions


Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models


Preventing dataset shift from breaking machine-learning biomarkers


Preventing pressure oscillations does not fix local linear stability issues of entropy-based split-form high-order schemes


Preventing sinking of a disk by leveraging the boundary jump phenomenon


Previous Knowledge Utilization In Online Anytime Belief Space Planning


Pri3D: Can 3D Priors Help 2D Representation Learning?


PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds


PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation


PriGen: Towards Automated Translation of Android Applications' Code to Privacy Captions


PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration


PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference


PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation


PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy


PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation


PriViT: Vision Transformers for Fast Private Inference


Priberam at MESINESP Multi-label Classification of Medical Texts Task


Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation


Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach


Price formation without fuel costs: the interaction of demand elasticity with storage bidding


Price graphs: Utilizing the structural information of financial time series for stock prediction


Price response functions and spread impact in correlated financial markets


Price-aware Recommendation with Graph Convolutional Networks


Pricing Engine: Estimating Causal Impacts in Real World Business Settings


Pricing European Options with Google AutoML, TensorFlow, and XGBoost


Pricing and Energy Trading in Peer-to-peer Zero Marginal-cost Microgrids


Pricing for Multi-modal Pickup and Delivery Problems with Heterogeneous Users


Pricing high-dimensional Bermudan options with hierarchical tensor formats


Pricing options on flow forwards by neural networks in Hilbert space


Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement


PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation


Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation


Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints


Primal-Dual Mesh Convolutional Neural Networks


Primal-Dual Neural Algorithmic Reasoning


Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction


Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots


Primal-dual block-proximal splitting for a class of non-convex problems


Primal-dual extragradient methods for nonlinear nonsmooth PDE-constrained optimization


Primal-dual hybrid gradient algorithms for computing time-implicit Hamilton-Jacobi equations


Primal-dual proximal splitting and generalized conjugation in non-smooth non-convex optimization


Primality Testing via Circulant Matrix Eigenvalue Structure: A Novel Approach Using Cyclotomic Field Theory


Primality proving using elliptic curves with complex multiplication by imaginary quadratic fields of class number three


Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning


Primary beam effects of radio astronomy antennas - II. Modelling the MeerKAT L-band beam


Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search


PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering


PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage


PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing


PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement


Primer AI's Systems for Acronym Identification and Disambiguation


Primer: Searching for Efficient Transformers for Language Modeling


Primes represented by shifted quadratic forms: on primitivity and congruence classes


Primitive Fitting Using Deep Boundary Aware Geometric Segmentation


Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation


Primitive abundant and weird numbers with many prime factors


Primitive-Swarm: An Ultra-lightweight and Scalable Planner for Large-scale Aerial Swarms


Primordial Black Holes and Induced Gravitational Waves from a Smooth Crossover beyond Standard Model


Primordial Black Holes as Silver Bullets for New Physics at the Weak Scale


Primordial Black Holes from Conformal Higgs


Primordial Bounce-Inflation Scenario to Alleviate Cosmological Tensions and Lensing Anomaly


Primordial non-Gaussianity and non-Gaussian Covariance


Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation


Principal Bit Analysis: Autoencoding with Schur-Concave Loss


Principal Component Analysis Using Structural Similarity Index for Images


Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction


Principal Component Analysis in the Graph Frequency Domain


Principal Component Hierarchy for Sparse Quadratic Programs


Principal Component Stochastic Subspace Identification for Output-Only Modal Analysis


Principal Components for Model-Agnostic Modified Gravity with 3x2pt


Principal Components for Neural Network Initialization


Principal Orthogonal Latent Components Analysis (POLCA Net)


Principal Stratification with Time-to-Event Outcomes


Principal Stratum Strategy: Potential Role in Drug Development


Principal component analysis balancing prediction and approximation accuracy for spatial data


Principal component analysis of the Chandra ACIS gain


Principal manifold estimation via model complexity selection


Principle of Relevant Information for Graph Sparsification


Principled Analyses and Design of First-Order Methods with Inexact Proximal Operators


Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning


Principled Bayesian Optimisation in Collaboration with Human Experts


Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples


Principled Data-Driven Decision Support for Cyber-Forensic Investigations


Principled Drug-Drug Interaction Terms for Bayesian Logistic Regression Models of Drug Safety in Oncology Phase I Combination Trials


Principled Exploration via Optimistic Bootstrapping and Backward Induction


Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting


Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4


Principled Multi-Aspect Evaluation Measures of Rankings


Principled Paraphrase Generation with Parallel Corpora


Principled Preferential Bayesian Optimization


Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors


Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization


Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric


Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models


Principled interpolation of Green's functions learned from data


Principled learning method for Wasserstein distributionally robust optimization with local perturbations


Principled network extraction from images


Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions


Principles of image reconstruction in optical interferometry: tutorial


Prink: $k_s$-Anonymization for Streaming Data in Apache Flink


Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping


Prior Attention Network for Multi-Lesion Segmentation in Medical Images


Prior Bilinear Based Models for Knowledge Graph Completion


Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference


Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models


Prior Effective Sample Size When Borrowing on the Treatment Effect Scale


Prior Knowledge Guided Unsupervised Domain Adaptation


Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images


Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization


Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval


Prior choice affects ability of Bayesian neural networks to identify unknowns


Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation


Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data


Prior-Constrained Association Learning for Fine-Grained Generalized Category Discovery


Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits


Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes


Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners


Prior-Free Continual Learning with Unlabeled Data in the Wild


Prior-Guided Adversarial Initialization for Fast Adversarial Training


Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays


Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy


PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning


PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer


Prioritize Alignment in Dataset Distillation


Prioritized Architecture Sampling with Monto-Carlo Tree Search


Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving


Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning


Prioritized Subnet Sampling for Resource-Adaptive Supernet Training


Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt


Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs


Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training


Prioritizing Investments in Cybersecurity: Empirical Evidence from an Event Study on the Determinants of Cyberattack Costs


Prioritizing emergency evacuations under compounding levels of uncertainty


Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding


Priority-based Parameter Propagation for Distributed DNN Training


Priors for second-order unbiased Bayes estimators


Prism Removes Consensus Bottleneck for Smart Contracts


Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs


Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems


Prismer: A Vision-Language Model with Multi-Task Experts


Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications


PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices


PrivAgent: Agentic-based Red-teaming for LLM Privacy Leakage


PrivCirNet: Efficient Private Inference via Block Circulant Transformation


PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy


PrivEdge: From Local to Distributed Private Training and Prediction


PrivFair: a Library for Privacy-Preserving Fairness Auditing


PrivGen: Preserving Privacy of Sequences Through Data Generation


PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information


PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining


PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models


PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model


PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance


Privacy Amplification by Decentralization


Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions


Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation


Privacy Amplification via Shuffling: Unified, Simplified, and Tightened


Privacy Attacks in Decentralized Learning


Privacy Attacks on Image AutoRegressive Models


Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run


Privacy Backdoors: Stealing Data with Corrupted Pretrained Models


Privacy Can Arise Endogenously in an Economic System with Learning Agents


Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges


Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control


Privacy Guarantees for De-identifying Text Transformations


Privacy Impact Assessments in the Wild: A Scoping Review


Privacy Implications of Retrieval-Based Language Models


Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation


Privacy Issues in Large Language Models: A Survey


Privacy Leakage of Adversarial Training Models in Federated Learning Systems


Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction


Privacy Limitations Of Interest-based Advertising On The Web: A Post-mortem Empirical Analysis Of Google's FLoC


Privacy Preference Signals: Past, Present and Future


Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach


Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation


Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data


Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks


Privacy Preserving Set-Based Estimation Using Partially Homomorphic Encryption


Privacy Protection in Distributed Fingerprint-based Authentication


Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training


Privacy Risks of Securing Machine Learning Models against Adversarial Examples


Privacy and Trust Redefined in Federated Machine Learning


Privacy enhanced collaborative inference in the Cox proportional hazards model for distributed data


Privacy for Free in the Overparameterized Regime


Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)


Privacy with Good Taste: A Case Study in Quantifying Privacy Risks in Genetic Scores


Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization


Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning


Privacy-Aware Randomized Quantization via Linear Programming


Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data


Privacy-Preserved Automated Scoring using Federated Learning for Educational Research


Privacy-Preserved Neural Graph Databases


Privacy-Preserved Neural Graph Similarity Learning


Privacy-Preserving Action Recognition via Motion Difference Quantization


Privacy-Preserving Adaptive Re-Identification without Image Transfer


Privacy-Preserving Autoencoder for Collaborative Object Detection


Privacy-Preserving Billing for Local Energy Markets


Privacy-Preserving Biometric Matching Using Homomorphic Encryption


Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression


Privacy-Preserving Classification with Secret Vector Machines


Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting


Privacy-Preserving Conformal Prediction Under Local Differential Privacy


Privacy-Preserving Continuous Event Data Publishing


Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming


Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption


Privacy-Preserving Cross-Domain Sequential Recommendation


Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)


Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis


Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning


Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting


Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework


Privacy-Preserving ECG Data Analysis with Differential Privacy: A Literature Review and A Case Study


Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems


Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation


Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent


Privacy-Preserving Graph Convolutional Networks for Text Classification


Privacy-Preserving Graph Embedding based on Local Differential Privacy


Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering


Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation


Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning


Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments


Privacy-Preserving Inference on the Ratio of Two Gaussians Using Sums


Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant


Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models


Privacy-Preserving Map-Free Exploration for Confirming the Absence of a Radioactive Source


Privacy-Preserving Maximum Matching on General Graphs and its Application to Enable Privacy-Preserving Kidney Exchange


Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features


Privacy-Preserving News Recommendation Model Learning


Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning


Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach


Privacy-Preserving Pose Estimation for Human-Robot Interaction


Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation


Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling


Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery


Privacy-Preserving Synthetic Data Generation for Recommendation Systems


Privacy-Preserving Synthetic Educational Data Generation


Privacy-Preserving Verifiable Neural Network Inference Service


Privacy-Preserving Video Conferencing via Thermal-Generative Images


Privacy-Preserving Visual Localization with Event Cameras


Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies


Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models


Privacy-Safe Iris Presentation Attack Detection


Privacy-aware Process Performance Indicators: Framework and Release Mechanisms


Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors


Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSA


Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory


Privacy-preserving Logistic Regression with Secret Sharing


Privacy-preserving Neural Representations of Text


Privacy-preserving Non-negative Matrix Factorization with Outliers


Privacy-preserving Similarity Calculation of Speaker Features Using Fully Homomorphic Encryption


Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach


Privacy-preserving data sharing via probabilistic modelling


Privacy-preserving datasets by capturing feature distributions with Conditional VAEs


Privacy-preserving machine learning with tensor networks


Privacy-preserving patient clustering for personalized federated learning


PrivacyDates: A Framework for More Privacy-Preserving Timestamp Data Types


PrivacyFL: A simulator for privacy-preserving and secure federated learning


Private Adaptive Optimization with Side Information


Private Aggregate Queries to Untrusted Databases


Private Approximate Query over Horizontal Data Federation


Private Attribute Inference from Images with Vision-Language Models


Private Blotto: Viewpoint Competition with Polarized Agents


Private Dataset Generation Using Privacy Preserving Collaborative Learning


Private Federated Learning using Preference-Optimized Synthetic Data


Private Frequency Estimation via Projective Geometry


Private Graph Extraction via Feature Explanations


Private Hierarchical Governance for Encrypted Messaging


Private Meeting Summarization Without Performance Loss


Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models


Private Non-Convex Federated Learning Without a Trusted Server


Private Outsourced Bayesian Optimization


Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?


Private Regression via Data-Dependent Sufficient Statistic Perturbation


Private Set Generation with Discriminative Information


Private Synthetic Text Generation with Diffusion Models


Private and Reliable Neural Network Inference


Private kNN-VC: Interpretable Anonymization of Converted Speech


Private, Anonymous, Collateralizable Commitments vs. MEV


Private, Auditable, and Distributed Ledger for Financial Institutes


Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging


PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services


Privately Connecting Mobility to Infectious Diseases via Applied Cryptography


Privately Learning from Graphs with Applications in Fine-tuning Large Language Models


Privately generating tabular data using language models


Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration


Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis


Prix-LM: Pretraining for Multilingual Knowledge Base Construction


Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding


ProAPO: Progressively Automatic Prompt Optimization for Visual Classification


ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences


ProAgent: From Robotic Process Automation to Agentic Process Automation


ProBF: Learning Probabilistic Safety Certificates with Barrier Functions


ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language


ProCIS: A Benchmark for Proactive Retrieval in Conversations


ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation


ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search


ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer


ProCaliper: functional and structural analysis, visualization, and annotation of proteins


ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification


ProCom: A Few-shot Targeted Community Detection Algorithm


ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation


ProDAG: Projected Variational Inference for Directed Acyclic Graphs


ProDOMA: improve PROtein DOMAin classification for third-generation sequencing reads using deep learning


ProDapt: Proprioceptive Adaptation using Long-term Memory Diffusion


ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion


ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation


ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification


ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models


ProFairRec: Provider Fairness-aware News Recommendation


ProFound: Source Extraction and Application to Modern Survey Data


ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees


ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization


ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction


ProGRes: Prompted Generative Rescoring on ASR n-Best


ProGS: Property Graph Shapes Language (Extended Version)


ProGen2: Exploring the Boundaries of Protein Language Models


ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback


ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations


ProGeny I: a new simple stellar population spectra generator and impact of isochrones / stellar atmospheres / initial mass functions


ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks


ProLFA: Representative Prototype Selection for Local Feature Aggregation


ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction


ProLab: perceptually uniform projective colour coordinate system


ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution


ProLiVis: Protein-Protein Interaction Literature Visualization System


ProMISe: Promptable Medical Image Segmentation using SAM


ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding


ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting


ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations


ProMix: Combating Label Noise via Maximizing Clean Sample Utility


ProPML: Probability Partial Multi-label Learning


ProQA: Structural Prompt-based Pre-training for Unified Question Answering


ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval


ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs


ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation


ProSLAM: Graph SLAM from a Programmer's Perspective


ProSec: Fortifying Code LLMs with Proactive Security Alignment


ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds


ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G


ProSpeCT: Provably Secure Speculation for the Constant-Time Policy (Extended version)


ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection


ProTeCt: Prompt Tuning for Taxonomic Open Set Classification


ProTo: Program-Guided Transformer for Program-Guided Tasks


ProTransformer: Robustify Transformers via Plug-and-Play Paradigm


ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context


ProVe -- Self-supervised pipeline for automated product replacement and cold-starting based on neural language models


ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models


Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance


Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion


Proactive Detection of Voice Cloning with Localized Watermarking


Proactive Interaction Framework for Intelligent Social Receptionist Robots


Proactive Prioritization of App Issues via Contrastive Learning


Proactive Query Expansion for Streaming Data Using External Source


Proactive Recommendation with Iterative Preference Guidance


Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals


Proactive Robot Assistance via Spatio-Temporal Object Modeling


Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach


Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags


Proactive security defense: cyber threat intelligence modeling for connected autonomous vehicles


Proactive slip control by learned slip model and trajectory adaptation


ProbAct: A Probabilistic Activation Function for Deep Neural Networks


ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling


ProbLP: A framework for low-precision probabilistic inference


ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification


ProbPose: A Probabilistic Approach to 2D Human Pose Estimation


ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments


ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE


ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models


Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution


Probabilistic $K$-mean with local alignment for clustering and motif discovery in functional data


Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter


Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures


Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models


Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness


Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering


Probabilistic Atlases to Enforce Topological Constraints


Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning


Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting


Probabilistic Bag-Of-Hyperlinks Model for Entity Linking


Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems


Probabilistic Best Subset Selection via Gradient-Based Optimization


Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning


Probabilistic Calibration by Design for Neural Network Regression


Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion


Probabilistic Circuits That Know What They Don't Know


Probabilistic Circuits for Variational Inference in Discrete Graphical Models


Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow


Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer


Probabilistic Compositional Embeddings for Multimodal Image Retrieval


Probabilistic Conformal Prediction Using Conditional Random Samples


Probabilistic Constrained Reinforcement Learning with Formal Interpretability


Probabilistic Contact State Estimation for Legged Robots using Inertial Information


Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs


Probabilistic Contrastive Learning for Long-Tailed Visual Recognition


Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)


Probabilistic Counting in Uncertain Spatial Databases using Generating Functions


Probabilistic Data Association for Semantic SLAM at Scale


Probabilistic Data Association via Mixture Models for Robust Semantic SLAM


Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics


Probabilistic Decomposition Transformer for Time Series Forecasting


Probabilistic Deep Metric Learning for Hyperspectral Image Classification


Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization


Probabilistic Delay Forecasting in 5G Using Recurrent and Attention-Based Architectures


Probabilistic Discriminative Learning with Layered Graphical Models


Probabilistic Domain Adaptation for Biomedical Image Segmentation


Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models


Probabilistic Emulation of a Global Climate Model with Spherical DYffusion


Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs


Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals


Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization


Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression


Probabilistic Forecasting of Irregular Time Series via Conditional Flows


Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN


Probabilistic Forecasting of the Masses and Radii of Other Worlds


Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization


Probabilistic Formal Modelling to Uncover and Interpret Interaction Styles


Probabilistic Formulation of the Take The Best Heuristic


Probabilistic Formulations of Regression with Mixed Guidance


Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties


Probabilistic Game-Theoretic Traffic Routing


Probabilistic Gathering Of Agents With Simple Sensors


Probabilistic Generative Transformer Language models for Generative Design of Molecules


Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression


Probabilistic Grammars for Equation Discovery


Probabilistic Graph Reasoning for Natural Proof Generation


Probabilistic Graph Rewiring via Virtual Nodes


Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views


Probabilistic Imputation for Time-series Classification with Missing Data


Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion


Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo


Probabilistic Inference of the Structure and Orbit of Milky Way Satellites with Semi-Analytic Modeling


Probabilistic Inference on Noisy Time Series (PINTS)


Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters


Probabilistic Iterative Hard Thresholding for Sparse Learning


Probabilistic Iterative LQR for Short Time Horizon MPC


Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference


Probabilistic Learning of Multivariate Time Series with Temporal Irregularity


Probabilistic Learning of Treatment Trees in Cancer


Probabilistic Linear Solvers for Machine Learning


Probabilistic Load Forecasting Based on Adaptive Online Learning


Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks


Probabilistic Matrix Factorization for Automated Machine Learning


Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios


Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning


Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction


Probabilistic Model for the Gravitational Wave Signal from Merging Black Holes


Probabilistic Modeling Using Tree Linear Cascades


Probabilistic Modeling for Human Mesh Recovery


Probabilistic Modeling for Sequences of Sets in Continuous-Time


Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation


Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows


Probabilistic Motion Planning under Temporal Tasks and Soft Constraints


Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data


Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning


Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets


Probabilistic Numerical Methods for Partial Differential Equations and Bayesian Inverse Problems


Probabilistic Object Maps for Long-Term Robot Localization


Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles


Probabilistic Parameter Estimation Using a Gaussian Mixture Density Network: Application to X-ray Reflectivity Data Curve Fitting


Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking


Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning


Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research


Probabilistic Pixel-Adaptive Refinement Networks


Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning


Probabilistic Population Protocol Models


Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models


Probabilistic Programming with Sufficient Statistics for faster Bayesian Computation


Probabilistic Programs with Stochastic Conditioning


Probabilistic Projections of Baseline 21st Century CO$_2$ Emissions Using a Simple Calibrated Integrated Assessment Model


Probabilistic Prompt Distribution Learning for Animal Pose Estimation


Probabilistic QoS-aware Placement of VNF chains at the Edge


Probabilistic Qualitative Localization and Mapping


Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty


Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula


Probabilistic Rainfall Estimation from Automotive Lidar


Probabilistic Random Forest: A machine learning algorithm for noisy datasets


Probabilistic Reach-Avoid for Bayesian Neural Networks


Probabilistic Reachable Set Estimation for Saturated Systems with Unbounded Additive Disturbances


Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image


Probabilistic Reconstruction of Type Ia Supernova SN 2002bo


Probabilistic Record Linkage of Two Gun Violence Data Sets


Probabilistic Relational Agent-based Models


Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration


Probabilistic Roadmaps for Aerial Relay Path Planning


Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes


Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings


Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search


Probabilistic Safety for Bayesian Neural Networks


Probabilistic Scores of Classifiers, Calibration is not Enough


Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions


Probabilistic Spatial Transformer Networks


Probabilistic Stability Assessment for Active Distribution Grids


Probabilistic State Estimation in Water Networks


Probabilistic Subgoal Representations for Hierarchical Reinforcement learning


Probabilistic Symmetry for Multi-Agent Dynamics


Probabilistic Topic Modelling with Transformer Representations


Probabilistic Traffic Forecasting with Dynamic Regression


Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation


Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design


Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation


Probabilistic Trust Intervals for Out of Distribution Detection


Probabilistic Verification of Fairness Properties via Concentration


Probabilistic Verification of Neural Networks using Branch and Bound


Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization


Probabilistic Visual Place Recognition for Hierarchical Localization


Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning


Probabilistic classification of X-ray sources applied to Swift-XRT and XMM-Newton catalogs


Probabilistic computation and uncertainty quantification with emerging covariance


Probabilistic deep learning approach for targeted hybrid organic-inorganic perovskites


Probabilistic description of dissipative chaotic scattering


Probabilistic embeddings for speaker diarization


Probabilistic emotion and sentiment modelling of patient-reported experiences


Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces


Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification


Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs


Probabilistic learning of boolean functions applied to the binary classification problem with categorical covariates


Probabilistic learning of the Purkinje network from the electrocardiogram


Probabilistic measures afford fair comparisons of AIWP and NWP model output


Probabilistic model for dynamic galaxy decomposition


Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network


Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment


Probabilistic models of genetic variation in structured populations applied to global human studies


Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing


Probabilistic neural networks for fluid flow surrogate modeling and data recovery


Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows


Probabilistic neural operators for functional uncertainty quantification


Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors


Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem model


Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling


Probabilistic programming methods for reconstruction of multichannel imaging detector events: ELVES and TRACK


Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models


Probabilistic road classification in historical maps using synthetic data and deep learning


Probabilistic selection of inducing points in sparse Gaussian processes


Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework


Probabilistic sequential matrix factorization


Probabilistic solar flare forecasting using historical magnetogram data


Probabilistic state synthesis based on optimal convex approximation


Probabilistic task modelling for meta-learning


Probabilistic volumetric speckle suppression in OCT using deep learning


Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments


Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising


Probabilistically Rewired Message-Passing Neural Networks


Probabilistically Robust Uncertainty Analysis and Optimal Control of Continuous Lyophilization via Polynomial Chaos Theory


Probabilistically safe and efficient model-based Reinforcement Learning


Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models


Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System


Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning


Probability Paths and the Structure of Predictions over Time


Probability Weighted Compact Feature for Domain Adaptive Retrieval


Probability and Structure of Quota Violations in the Huntington-Hill Apportionment Method for 3 States


Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and cost-effectiveness analysis


Probability vector representation of the Schrödinger equation and Leggett-Garg-type experiments


Probability-Consistent Preference Optimization for Enhanced LLM Reasoning


Probability-based Global Cross-modal Upsampling for Pansharpening


Probable Domain Generalization via Quantile Risk Minimization


Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation


Probablistic determination of mod-$3$ Galois images of principally polarized abelian surfaces over the rationals


Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits


Probably Approximate Shapley Fairness with Applications in Machine Learning


Probably Approximately Correct Vision-Based Planning using Motion Primitives


Probably Correct Optimal Stable Matching for Two-Sided Markets Under Uncertainty


Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing


Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models


Probe incompatibility in multiparameter noisy quantum metrology


Probeable DARTS with Application to Computational Pathology


Probeable Problems for Beginner-level Programming-with-AI Contests


Probing 2D Asymmetries of an Exoplanet Atmosphere from Chromatic Transit Variation


Probing Across Time: What Does RoBERTa Know and When?


Probing BERT's priors with serial reproduction chains


Probing Benchmark Models of Hidden-Sector Dark Matter with DAMIC-M


Probing Biomedical Embeddings from Language Models


Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary


Probing Conceptual Understanding of Large Visual-Language Models


Probing Contextual Diversity for Dense Out-of-Distribution Detection


Probing Critical Learning Dynamics of PLMs for Hate Speech Detection


Probing Dark Matter Electromagnetic Properties in Direct Detection Experiments


Probing Dark Matter Substructures with Free-Form Modelling: A Case Study of the `Jackpot' Strong Lens


Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better


Probing Image-Language Transformers for Verb Understanding


Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck Equation


Probing In-Context Learning: Impact of Task Complexity and Model Architecture on Generalization and Efficiency


Probing Jet-Torus Interaction in the Radio Galaxy NGC 1052 by Sulfur-Bearing Molecules


Probing LLMs for Joint Encoding of Linguistic Categories


Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set


Probing Language Models for Pre-training Data Detection


Probing Language Models for Understanding of Temporal Expressions


Probing Language Models on Their Knowledge Source


Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics


Probing Light Fermiophobic Higgs Boson via diphoton jets at the HL-LHC


Probing Linguistic Information For Logical Inference In Pre-trained Language Models


Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case


Probing Multimodal LLMs as World Models for Driving


Probing Multimodal Large Language Models for Global and Local Semantic Representations


Probing Neural Dialog Models for Conversational Understanding


Probing Neural Network Comprehension of Natural Language Arguments


Probing Neural Topology of Large Language Models


Probing Pre-Trained Language Models for Cross-Cultural Differences in Values


Probing Pre-Trained Language Models for Disease Knowledge


Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition


Probing Predictions on OOD Images via Nearest Categories


Probing Pretrained Models of Source Code


Probing Secret Interactions of Astrophysical Neutrinos in the High-Statistics Era


Probing Simile Knowledge from Pre-trained Language Models


Probing Small-Scale Power Spectra with Pulsar Timing Arrays


Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks


Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective


Probing alternative cosmologies through the inverse distance ladder


Probing circular polarization and magnetic field structure in AGN


Probing electron-phonon interactions away from the Fermi level with resonant inelastic x-ray scattering


Probing for Bridging Inference in Transformer Language Models


Probing for Constituency Structure in Neural Language Models


Probing for Incremental Parse States in Autoregressive Language Models


Probing for Multilingual Numerical Understanding in Transformer-Based Language Models


Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings


Probing for targeted syntactic knowledge through grammatical error detection


Probing growth precursor diffusion lengths by inter-surface diffusion


Probing minihalo lenses with diffracted gravitational waves


Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models


Probing omics data via harmonic persistent homology


Probing photon-ALP oscillations from the flat spectrum radio quasar 4C+21.35


Probing quantum entanglement from magnetic-sublevels populations: beyond spin squeezing inequalities


Probing small-scale baryon and dark matter isocurvature perturbations with cosmic microwave background anisotropies


Probing the 3D Awareness of Visual Foundation Models


Probing the CP structure of the top quark Yukawa coupling: Loop sensitivity vs. on-shell sensitivity


Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction


Probing the Consistency of Cosmological Contours for Supernova Cosmology


Probing the Creativity of Large Language Models: Can models produce divergent semantic association?


Probing the Decision Boundaries of In-context Learning in Large Language Models


Probing the Delay Time of Supermassive Black Hole Binary Mergers With Gravitational Waves


Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks


Probing the Information Theoretical Roots of Spatial Dependence Measures


Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games


Probing the Natural Language Inference Task with Automated Reasoning Tools


Probing the Physics of Star-Formation (ProPStar) III. No evidence for dissipation of turbulence down to 20 mpc (4 000 au) scale


Probing the Physics of Star-Formation (ProPStar): II. The first systematic search for streamers toward protostars


Probing the Robustness Properties of Neural Speech Codecs


Probing the Robustness of Large Language Models Safety to Latent Perturbations


Probing the Robustness of Time-series Forecasting Models with CounterfacTS


Probing the Robustness of Trained Metrics for Conversational Dialogue Systems


Probing the Theoretical and Computational Limits of Dissipative Design


Probing the dark sector with nuclear transition photons


Probing the effects of broken symmetries in machine learning


Probing the efficiency of tidal synchronization in outspiralling double white dwarf binaries with LISA


Probing the phonetic and phonological knowledge of tones in Mandarin TTS models


Probing the physics of star formation (ProPStar): I. First resolved maps of the electron fraction and cosmic-ray ionization rate in NGC 1333


Probing the ringdown perturbation in binary black hole coalescences with an improved quasi-normal mode extraction algorithm


Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia


Probing then Editing Response Personality of Large Language Models


Probing thermal effects on static spacetimes with Unruh-DeWitt detectors


Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs


Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks


Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN


Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations


Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems


Problem-Solving in Language Model Networks


Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits


Problematic Tokens: Tokenizer Bias in Large Language Models


Problems of representation of electrocardiograms in convolutional neural networks


Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words


ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure


ProcData: An R Package for Process Data Analysis


Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models


Procedural Fairness Through Decoupling Objectionable Data Generating Components


Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning


Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool


Procedural Generation of 3D Maps with Snappable Meshes


Procedural Generation of Complex Roundabouts for Autonomous Vehicle Testing


Procedural Image Programs for Representation Learning


Procedural Low-Poly Terrain Generation with Terracing for Computer Games


Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks


Procedural Synthesis of Synthesizable Molecules


Procedural Text Mining with Large Language Models


Procedural generation of meta-reinforcement learning tasks


Procedural terrain generation with style transfer


Procedurally Optimised ZX-Diagram Cutting for Efficient T-Decomposition in Classical Simulation


Procedurally Provisioned Access Control for Robotic Systems


Proceduray -- A light-weight engine for procedural primitive ray tracing


Procedure-Aware Pretraining for Instructional Video Understanding


Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine


Proceedings of the 5th International Workshop on Reading Music Systems


Process Channels: A New Layer for Process Enactment Based on Blockchain State Channels


Process Discovery Using Graph Neural Networks


Process Mining Embeddings: Learning Vector Representations for Petri Nets


Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs


Process Model Forecasting Using Time Series Analysis of Event Sequence Data


Process Modeling With Large Language Models


Process Monitoring on Sequences of System Call Count Vectors


Process Query Language: Design, Implementation, and Evaluation


Process Reward Model with Q-Value Rankings


Process Reward Models for LLM Agents: Practical Framework and Directions


Process Trace Querying using Knowledge Graphs and Notation3


Process flowsheet optimization with surrogate and implicit formulations of a Gibbs reactor


Process-BERT: A Framework for Representation Learning on Educational Process Data


Process-Driven Autoformalization in Lean 4


Process-Supervised LLM Recommenders via Flow-guided Tuning


Process-Supervised Reward Models for Clinical Note Generation: A Scalable Approach Guided by Domain Expertise


Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking


Process-based Self-Rewarding Language Models


Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems


ProcessBench: Identifying Process Errors in Mathematical Reasoning


ProcessPainter: Learn Painting Process from Sequence Data


ProcessTBench: An LLM Plan Generation Dataset for Process Mining


ProcessTransformer: Predictive Business Process Monitoring with Transformer Network