Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems


Learning an Adaptive Model for Extreme Low-light Raw Image Processing


Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking


Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation


Learning an Efficient Multimodal Depth Completion Model


Learning an Interpretable Model for Driver Behavior Prediction with Inductive Biases


Learning an Interpretable Traffic Signal Control Policy


Learning an Overlap-based Observation Model for 3D LiDAR Localization


Learning an Unreferenced Metric for Online Dialogue Evaluation


Learning an Urban Air Mobility Encounter Model from Expert Preferences


Learning an optimal PSF-pair for ultra-dense 3D localization microscopy


Learning and Analyzing Generation Order for Undirected Sequence Models


Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy


Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration


Learning and Evaluating Emotion Lexicons for 91 Languages


Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning


Learning and Evaluating Representations for Deep One-class Classification


Learning and Generalization in Overparameterized Normalizing Flows


Learning and Interpreting Potentials for Classical Hamiltonian Systems


Learning and Planning in Average-Reward Markov Decision Processes


Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks


Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games


Learning and avoiding disorder in multimode fibers


Learning and controlling the source-filter representation of speech with a variational autoencoder


Learning and forecasting of age-specific period mortality via B-spline processes with locally-adaptive dynamic coefficients


Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency


Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition


Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling


Learning atomic forces from uncertainty-calibrated adversarial attacks


Learning based signal detection for MIMO systems with unknown noise statistics


Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection


Learning big logical rules by joining small rules


Learning body-affordances to simplify action spaces


Learning by Analogy: Diverse Questions Generation in Math Word Problem


Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation


Learning by Competition of Self-Interested Reinforcement Learning Agents


Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning


Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning


Learning by Fixing: Solving Math Word Problems with Weak Supervision


Learning by Planning: Language-Guided Global Image Editing


Learning by Playing - Solving Sparse Reward Tasks from Scratch


Learning by Semantic Similarity Makes Abstractive Summarization Better


Learning by Sorting: Self-supervised Learning with Group Ordering Constraints


Learning by Turning: Neural Architecture Aware Optimisation


Learning chemical reaction networks from trajectory data


Learning collective multi-cellular dynamics from temporal scRNA-seq via a transformer-enhanced Neural SDE


Learning collective variables that preserve transition rates


Learning complexity gradually in quantum machine learning models


Learning compositional functions via multiplicative weight updates


Learning conditional distributions on continuous spaces


Learning cooperative behaviours in adversarial multi-agent systems


Learning correspondences of cardiac motion from images using biomechanics-informed modeling


Learning cortical representations through perturbed and adversarial dreaming


Learning cosmology and clustering with cosmic graphs


Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages


Learning curves for the multi-class teacher-student perceptron


Learning curves of generic features maps for realistic datasets with a teacher-student model


Learning data efficient coarse-grained molecular dynamics from forces and noise


Learning deep illumination-robust features from multispectral filter array images


Learning deep kernels for exponential family densities


Learning deep multiresolution representations for pansharpening


Learning deep representations for video-based intake gesture detection


Learning differential equation models from stochastic agent-based model simulations


Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves


Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia


Learning diverse attacks on large language models for robust red-teaming and safety tuning


Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation


Learning dominant physical processes with data-driven balance models


Learning dynamic representations of the functional connectome in neurobiological networks


Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series


Learning dynamical systems: an example from open quantum system dynamics


Learning effective good variables from physical data


Learning effective pruning at initialization from iterative pruning


Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning


Learning equivariant tensor functions with applications to sparse vector recovery


Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling


Learning explanations that are hard to vary


Learning fair representation with a parametric integral probability metric


Learning fast and agile quadrupedal locomotion over complex terrain


Learning filtered discretization operators: non-intrusive versus intrusive approaches


Learning finite difference methods for reaction-diffusion type equations with FCNN


Learning finite symmetry groups of dynamical systems via equivariance detection


Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)


Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification


Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees


Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation


Learning for Online Mixed-Integer Model Predictive Control with Parametric Optimality Certificates


Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication


Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model


Learning force fields from stochastic trajectories


Learning from #Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods


Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning


Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction


Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality


Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer


Learning from Children: Improving Image-Caption Pretraining via Curriculum


Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review


Learning from Context or Names? An Empirical Study on Neural Relation Extraction


Learning from Convolution-based Unlearnable Datasets


Learning from Counterfactual Links for Link Prediction


Learning from Demonstration without Demonstrations


Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge


Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation


Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks


Learning from Explanations with Neural Execution Tree


Learning from Fact-checkers: Analysis and Generation of Fact-checking Language


Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales


Learning from Future: A Novel Self-Training Framework for Semantic Segmentation


Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs


Learning from Heterogeneous Data Based on Social Interactions over Graphs


Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks


Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration


Learning from Human Directional Corrections


Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues


Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding


Learning from Integral Losses in Physics Informed Neural Networks


Learning from Irregularly-Sampled Time Series: A Missing Data Perspective


Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization


Learning from Label Proportions by Learning with Label Noise


Learning from Label Proportions with Generative Adversarial Networks


Learning from Label Proportions with Instance-wise Consistency


Learning from Label Relationships in Human Affect


Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features


Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition


Learning from Mistakes via Cooperative Study Assistant for Large Language Models


Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training


Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model


Learning from Multi-domain Artistic Images for Arbitrary Style Transfer


Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion


Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction


Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT


Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning


Learning from Multiple Sources for Data-to-Text and Text-to-Data


Learning from Naturally Occurring Feedback


Learning from Negative Samples in Generative Biomedical Entity Linking


Learning from Noisy Anchors for One-stage Object Detection


Learning from Noisy Crowd Labels with Logics


Learning from Noisy Labels for Entity-Centric Information Extraction


Learning from Noisy Labels via Conditional Distributionally Robust Optimization


Learning from Noisy Labels with Decoupled Meta Label Purifier


Learning from Noisy Labels with Deep Neural Networks: A Survey


Learning from Noisy Pseudo-labels for All-Weather Land Cover Mapping


Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning


Learning from Pattern Completion: Self-supervised Controllable Generation


Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method


Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting


Learning from Positive and Unlabeled Data with Arbitrary Positive Shift


Learning from Reduced Labels for Long-Tailed Data


Learning from Response not Preference: A Stackelberg Approach for LLM Detoxification using Non-parallel Data


Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection


Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification


Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition


Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness


Learning from Sparse Offline Datasets via Conservative Density Estimation


Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation


Learning from Streaming Data when Users Choose


Learning from Suboptimal Demonstration via Self-Supervised Reward Regression


Learning from Successful and Failed Demonstrations via Optimization


Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes


Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection


Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition


Learning from Temporal Gradient for Semi-supervised Action Recognition


Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition


Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features


Learning from Trajectories via Subgoal Discovery


Learning from True-False Labels via Multi-modal Prompt Retrieving


Learning from Uncurated Regular Expressions


Learning from Unlabeled 3D Environments for Vision-and-Language Navigation


Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping


Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health


Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries


Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests


Learning from aggregated data with a maximum entropy model


Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits


Learning from deep learning: better cosmological parameter inference from weak lensing maps


Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks


Learning from imperfect training data using a robust loss function: application to brain image segmentation


Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science


Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking


Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage


Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses


Learning from the Tangram to Solve Mini Visual Tasks


Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation


Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection


Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset


Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data


Learning future terrorist targets through temporal meta-graphs


Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as the pilot case


Learning graph representations of biochemical networks and its application to enzymatic link prediction


Learning grid cells by predictive coding


Learning grounded word meaning representations on similarity graphs


Learning heterogeneous delays in a layer of spiking neurons for fast motion detection


Learning high-level visual representations from a child's perspective without strong inductive biases


Learning how to Active Learn: A Deep Reinforcement Learning Approach


Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs


Learning how to surf: Reconstructing the propagation and origin of gravitational waves with Gaussian Processes


Learning idempotent representation for subspace clustering


Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE


Learning image representations for anomaly detection: application to discovery of histological alterations in drug development


Learning in Conjectural Stackelberg Games


Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models


Learning in Deep Factor Graphs with Gaussian Belief Propagation


Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training


Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication


Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms


Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium


Learning in Stackelberg Games with Non-myopic Agents


Learning in Wilson-Cowan model for metapopulation


Learning in latent spaces improves the predictive accuracy of deep neural operators


Learning in the Machine: To Share or Not to Share?


Learning in the Wild with Incremental Skeptical Gaussian Processes


Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models


Learning in two-player games between transparent opponents


Learning interaction rules from multi-animal trajectories via augmented behavioral models


Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps


Learning invariant representations of time-homogeneous stochastic dynamical systems


Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets


Learning knot invariants across dimensions


Learning landmark geodesics using Kalman ensembles


Learning language variations in news corpora through differential embeddings


Learning latent causal graphs via mixture oracles


Learning latent representations across multiple data domains using Lifelong VAEGAN


Learning local discrete features in explainable-by-design convolutional neural networks


Learning local neighborhoods of non-Gaussian graphical models: A measure transport approach


Learning logic programs by combining programs


Learning logic programs by discovering higher-order abstractions


Learning logic programs by discovering where not to search


Learning logic programs through divide, constrain, and conquer


Learning long-range spatial dependencies with horizontal gated-recurrent units


Learning long-term dependencies for action recognition with a biologically-inspired deep network


Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods


Learning low-rank latent mesoscale structures in networks


Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis


Learning metrics for persistence-based summaries and applications for graph classification


Learning minimal representations of stochastic processes with variational autoencoders


Learning minimal volume uncertainty ellipsoids


Learning monocular depth estimation infusing traditional stereo knowledge


Learning monocular depth estimation with unsupervised trinocular assumptions


Learning more expressive joint distributions in multimodal variational methods


Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives


Learning multi-scale local conditional probability models of images


Learning multiple gaits of quadruped robot using hierarchical reinforcement learning


Learning multiview 3D point cloud registration


Learning music audio representations via weak language supervision


Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data


Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting


Learning neural state-space models: do we need a state estimator?


Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage


Learning non-parametric Markov networks with mutual information


Learning nonequilibrium control forces to characterize dynamical phase transitions


Learning nonequilibrium statistical mechanics and dynamical phase transitions


Learning nonlocal constitutive models with neural networks


Learning nonparametric DAGs with incremental information via high-order HSIC


Learning normal asymmetry representations for homologous brain structures


Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations


Learning normalized image densities via dual score matching


Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation


Learning of Colors from Color Names: Distribution and Point Estimation


Learning of Discrete Graphical Models with Neural Networks


Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data


Learning of Parameters in Behavior Trees for Movement Skills


Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction


Learning of discrete models of variational PDEs from data


Learning on Graphs with Out-of-Distribution Nodes


Learning on LLM Output Signatures for gray-box LLM Behavior Analysis


Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning


Learning optimal integration of spatial and temporal information in noisy chemotaxis


Learning optimal objective values for MILP


Learning optimal wavelet bases using a neural network approach


Learning or Self-aligning? Rethinking Instruction Fine-tuning


Learning orbital dynamics of binary black hole systems from gravitational wave measurements


Learning ordered pooling weights in image classification


Learning out-of-time-ordered correlators with classical kernel methods


Learning outside the Black-Box: The pursuit of interpretable models


Learning pairwise Markov network structures using correlation neighborhoods


Learning partial differential equations for biological transport models from noisy spatiotemporal data


Learning permutation symmetries with gips in R


Learning perturbation sets for robust machine learning


Learning phase transitions in ferrimagnetic GdFeCo alloys


Learning phylogenetic trees as hyperbolic point configurations


Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES


Learning policies for resource allocation in business processes


Learning poly-synaptic paths with traveling waves


Learning predictable and robust neural representations by straightening image sequences


Learning primal-dual sparse kernel machines


Learning production functions for supply chains with graph neural networks


Learning pronunciation from a foreign language in speech synthesis networks


Learning proofs for the classification of nilpotent semigroups


Learning protein sequence embeddings using information from structure


Learning quantum data with the quantum Earth Mover's distance


Learning quantum dynamics with latent neural ODEs


Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses


Learning recurrent dynamics in spiking networks


Learning relevant features for statistical inference


Learning representations of learning representations


Learning representations that are closed-form Monge mapping optimal with application to domain adaptation


Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes


Learning selection strategies in Buchberger's algorithm


Learning semantic sentence representations from visually grounded language without lexical knowledge


Learning single-image 3D reconstruction by generative modelling of shape, pose and shading


Learning soft interventions in complex equilibrium systems


Learning sources of variability from high-dimensional observational studies


Learning sparse features can lead to overfitting in neural networks


Learning spatio-temporal patterns with Neural Cellular Automata


Learning spectro-temporal representations of complex sounds with parameterized neural networks


Learning step sizes for unfolded sparse coding


Learning stochastic dynamics and predicting emergent behavior using transformers


Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels


Learning task structure via sparsity grouped multitask learning


Learning temporal structure of the input with a network of integrate-and-fire neurons


Learning test generators for cyber-physical systems


Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise


Learning the Crystal Structure Genome for Property Classification


Learning the Degradation Distribution for Blind Image Super-Resolution


Learning the Delay Using Neural Delay Differential Equations


Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting


Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks


Learning the Dynamics of Sparsely Observed Interacting Systems


Learning the Effects of Physical Actions in a Multi-modal Environment


Learning the Enigma with Recurrent Neural Networks


Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting


Learning the Expected Core of Strictly Convex Stochastic Cooperative Games


Learning the Geodesic Embedding with Graph Neural Networks


Learning the Implicit Semantic Representation on Graph-Structured Data


Learning the Ising Model with Generative Neural Networks


Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction


Learning the Language of Protein Structure


Learning the Lantern: Neural network applications to broadband photonic lantern modelling


Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference


Learning the Legibility of Visual Text Perturbations


Learning the Markov Decision Process in the Sparse Gaussian Elimination


Learning the Model Update for Siamese Trackers


Learning the Next Best View for 3D Point Clouds via Topological Features


Learning the Optimal Power Flow: Environment Design Matters


Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test


Learning the Pareto Front with Hypernetworks


Learning the Precise Feature for Cluster Assignment


Learning the Predictability of the Future


Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows


Learning the Relation between Code Features and Code Transforms with Structured Prediction


Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning


Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs


Learning the Simplicity of Scattering Amplitudes


Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks


Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling


Learning the Structure of Large Networked Systems Obeying Conservation Laws


Learning the Subsystem of Local Planning for Autonomous Racing


Learning the Superpixel in a Non-iterative and Lifelong Manner


Learning the Tangent Space of Dynamical Instabilities from Data


Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis


Learning the Universe: GalactISM simulations of resolved star formation and galactic outflows across main sequence and quenched galactic environments


Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks


Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning


Learning the distribution with largest mean: two bandit frameworks


Learning the dynamics of technical trading strategies


Learning the exchange-correlation functional from nature with fully differentiable density functional theory


Learning the galaxy-environment connection with graph neural networks


Learning the greatest common divisor: explaining transformer predictions


Learning the helix topology of musical pitch


Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data


Learning the kernel matrix via predictive low-rank approximations


Learning the meanings of function words from grounded language using a visual question answering model


Learning the mechanisms of network growth


Learning the noise fingerprint of quantum devices


Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks


Learning the optimal Tikhonov regularizer for inverse problems


Learning the physics-consistent material behavior from experimentally measurable data via PDE-constrained optimization


Learning the price response of active distribution networks for TSO-DSO coordination


Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy


Learning the rules of peptide self-assembly through data mining with large language models


Learning the smoothness of noisy curves with application to online curve estimation


Learning the spatio-temporal relationship between wind and significant wave height using deep learning


Learning the structure of Bayesian Networks via the bootstrap


Learning the subspace of variation for global optimization of functions with low effective dimension


Learning thermodynamically constrained equations of state with uncertainty


Learning to "Segment Anything" in Thermal Infrared Images through Knowledge Distillation with a Large Scale Dataset SATIR


Learning to Abstract and Predict Human Actions


Learning to Accelerate Decomposition for Multi-Directional 3D Printing


Learning to Accelerate Partial Differential Equations via Latent Global Evolution


Learning to Act with Affordance-Aware Multimodal Neural SLAM


Learning to Actively Reduce Memory Requirements for Robot Control Tasks


Learning to Adapt Clinical Sequences with Residual Mixture of Experts


Learning to Adapt to Position Bias in Vision Transformer Classifiers


Learning to Adapt to Unseen Abnormal Activities under Weak Supervision


Learning to Adversarially Blur Visual Object Tracking


Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification


Learning to Annotate Part Segmentation with Gradient Matching


Learning to Answer Questions in Dynamic Audio-Visual Scenarios


Learning to Anticipate Future with Dynamic Context Removal


Learning to Approximate Functions Using Nb-doped SrTiO$_3$ Memristors


Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation


Learning to Ask Conversational Questions by Optimizing Levenshtein Distance


Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information


Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain


Learning to Ask Medical Questions using Reinforcement Learning


Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders


Learning to Ask: Question-based Sequential Bayesian Product Search


Learning to Assimilate in Chaotic Dynamical Systems


Learning to Assist Humans without Inferring Rewards


Learning to Augment Distributions for Out-of-Distribution Detection


Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison


Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts


Learning to Avoid Errors in GANs by Manipulating Input Spaces


Learning to Balance Specificity and Invariance for In and Out of Domain Generalization


Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks


Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games


Learning to Bid Without Knowing your Value


Learning to Boost the Performance of Stable Nonlinear Systems


Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data


Learning to Bound: A Generative Cramér-Rao Bound


Learning to Brachiate via Simplified Model Imitation


Learning to Branch: Accelerating Resource Allocation in Wireless Networks


Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash


Learning to Build by Building Your Own Instructions


Learning to Caption Images through a Lifetime by Asking Questions


Learning to Caricature via Semantic Shape Transform


Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems


Learning to Characterize Matching Experts


Learning to Check Contract Inconsistencies


Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models


Learning to Classify Open Intent via Soft Labeling and Manifold Mixup


Learning to Code on Graphs for Topological Interference Management


Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach


Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning


Learning to Combine Per-Example Solutions for Neural Program Synthesis


Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules


Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot Collaboration


Learning to Communicate with Strangers via Channel Randomisation Methods


Learning to Compare Hardware Designs for High-Level Synthesis


Learning to Compare Image Patches via Convolutional Neural Networks


Learning to Compare Nodes in Branch and Bound with Graph Neural Networks


Learning to Complement and to Defer to Multiple Users


Learning to Compose Domain-Specific Transformations for Data Augmentation


Learning to Compose Hypercolumns for Visual Correspondence


Learning to Compose Soft Prompts for Compositional Zero-Shot Learning


Learning to Compose SuperWeights for Neural Parameter Allocation Search


Learning to Compose with Professional Photographs on the Web


Learning to Compress Prompts with Gist Tokens


Learning to Compress Videos without Computing Motion


Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias


Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder


Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling


Learning to Continually Learn Rapidly from Few and Noisy Data


Learning to Continually Learn with the Bayesian Principle


Learning to Control Local Search for Combinatorial Optimization


Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity


Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control


Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections


Learning to Cooperate with Humans using Generative Agents


Learning to Copy for Automatic Post-Editing


Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning


Learning to Correct for QA Reasoning with Black-box LLMs


Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor


Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision


Learning to Count Isomorphisms with Graph Neural Networks


Learning to Deblur using Light Field Generated and Real Defocus Images


Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation


Learning to Deceive in Multi-Agent Hidden Role Games


Learning to Decode Collaboratively with Multiple Language Models


Learning to Decompose and Disentangle Representations for Video Prediction


Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles


Learning to Defer to a Population: A Meta-Learning Approach


Learning to Delegate for Large-scale Vehicle Routing


Learning to Denoise Astronomical Images with U-nets


Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction


Learning to Denoise Distantly-Labeled Data for Entity Typing


Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale


Learning to Describe Differences Between Pairs of Similar Images


Learning to Describe Phrases with Local and Global Contexts


Learning to Describe Solutions for Bug Reports Based on Developer Discussions


Learning to Describe for Predicting Zero-shot Drug-Drug Interactions


Learning to Design Analog Circuits to Meet Threshold Specifications


Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images


Learning to Detect Mobile Objects from LiDAR Scans Without Labels


Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt


Learning to Detect Multiple Photographic Defects


Learning to Detect Noisy Labels Using Model-Based Features


Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels


Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation


Learning to Discover Efficient Mathematical Identities


Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach


Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition


Learning to Discover Novel Visual Categories via Deep Transfer Clustering


Learning to Discretely Compose Reasoning Module Networks for Video Captioning


Learning to Discretize Denoising Diffusion ODEs


Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification


Learning to Disentangle Scenes for Person Re-identification


Learning to Dissipate Energy in Oscillatory State-Space Models


Learning to Distill Global Representation for Sparse-View CT


Learning to Diversify for Single Domain Generalization


Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning


Learning to Downsample for Segmentation of Ultra-High Resolution Images


Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning


Learning to Draw: Emergent Communication through Sketching


Learning to Drop Points for LiDAR Scan Synthesis


Learning to Edit Visual Programs with Self-Supervision


Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging


Learning to Estimate External Forces of Human Motion in Video


Learning to Estimate Hidden Motions with Global Motion Aggregation


Learning to Estimate Pose by Watching Videos


Learning to Estimate RIS-Aided mmWave Channels


Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes


Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision


Learning to Estimate and Refine Fluid Motion with Physical Dynamics


Learning to Evaluate Performance of Multi-modal Semantic Localization


Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks


Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics


Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models


Learning to Explain: Answering Why-Questions via Rephrasing


Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering


Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs


Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation


Learning to Explore for Stochastic Gradient MCMC


Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization


Learning to Explore using Active Neural SLAM


Learning to Extend Molecular Scaffolds with Structural Motifs


Learning to Extract Building Footprints from Off-Nadir Aerial Images


Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices


Learning to Extract Structured Entities Using Language Models


Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction


Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks


Learning to Filter Context for Retrieval-Augmented Generation


Learning to Filter: Siamese Relation Network for Robust Tracking


Learning to Find Common Objects Across Few Image Collections


Learning to Follow Instructions in Text-Based Games


Learning to Follow Object-Centric Image Editing Instructions Faithfully


Learning to Forecast Dynamical Systems from Streaming Data


Learning to Fuse Asymmetric Feature Maps in Siamese Trackers


Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes


Learning to Fuse Music Genres with Generative Adversarial Dual Learning


Learning to Fuse Sentences with Transformers for Summarization


Learning to Gather without Communication


Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks


Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification


Learning to Generalize across Domains on Single Test Samples


Learning to Generalize for Cross-domain QA


Learning to Generalize for Sequential Decision Making


Learning to Generalize from Sparse and Underspecified Rewards


Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation


Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter


Learning to Generate Better Than Your LLM


Learning to Generate Chairs with Generative Adversarial Nets


Learning to Generate Compositional Color Descriptions


Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues


Learning to Generate Corrective Patches using Neural Machine Translation


Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models


Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers


Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation


Learning to Generate Levels From Nothing


Learning to Generate Music With Sentiment


Learning to Generate Noise for Multi-Attack Robustness


Learning to Generate Realistic LiDAR Point Clouds


Learning to Generate Scene Graph from Natural Language Supervision


Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis


Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation


Learning to Generate Task-Specific Adapters from Task Description


Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs


Learning to Generate Unit Tests for Automated Debugging


Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata


Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories


Learning to Guide and to Be Guided in the Architect-Builder Problem


Learning to Handle Complex Constraints for Vehicle Routing Problems


Learning to Hash for Recommendation: A Survey


Learning to Identify Critical States for Reinforcement Learning from Videos


Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach


Learning to Identify Perceptual Bugs in 3D Video Games


Learning to Identify Top Elo Ratings: A Dueling Bandits Approach


Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks


Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data


Learning to Imagine: Visually-Augmented Natural Language Generation


Learning to Improve Image Compression without Changing the Standard Decoder


Learning to Incorporate Structure Knowledge for Image Inpainting


Learning to Index for Nearest Neighbor Search


Learning to Infer Generative Template Programs for Visual Concepts


Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference


Learning to Influence Vehicles' Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars


Learning to Intervene on Concept Bottlenecks


Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning


Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer


Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling


Learning to Learn Better for Video Object Segmentation


Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning


Learning to Learn Single Domain Generalization


Learning to Learn Variational Semantic Memory


Learning to Learn by Zeroth-Order Oracle


Learning to Learn from APIs: Black-Box Data-Free Meta-Learning


Learning to Learn from Noisy Labeled Data


Learning to Learn with Generative Models of Neural Network Checkpoints


Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference


Learning to Learn without Forgetting using Attention


Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game


Learning to Localize Actions from Moments


Learning to Localize Leakage of Cryptographic Sensitive Variables


Learning to Localize Temporal Events in Large-scale Video Data


Learning to Localize in New Environments from Synthetic Training Data


Learning to Localize in Unseen Scenes with Relative Pose Regressors


Learning to Locate Visual Answer in Video Corpus Using Question


Learning to Make Analogies by Contrasting Abstract Relational Structure


Learning to Manipulate Deformable Objects without Demonstrations


Learning to Manipulate Individual Objects in an Image


Learning to Manipulate under Limited Information


Learning to Map Context-Dependent Sentences to Executable Formal Queries


Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training


Learning to Match Mathematical Statements with Proofs


Learning to Maximize Mutual Information for Chain-of-Thought Distillation


Learning to Maximize Mutual Information for Dynamic Feature Selection


Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms


Learning to Mediate Disparities Towards Pragmatic Communication


Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues


Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers


Learning to Minimize the Remainder in Supervised Learning


Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting


Learning to Model and Calibrate Optics via a Differentiable Wave Optics Simulator


Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles


Learning to Model the Relationship Between Brain Structural and Functional Connectomes


Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout


Learning to Negotiate via Voluntary Commitment


Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations


Learning to Obstruct Few-Shot Image Classification over Restricted Classes


Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees


Learning to Optimize for Reinforcement Learning


Learning to Orient Surfaces by Self-supervised Spherical CNNs


Learning to Pan-sharpening with Memories of Spatial Details


Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation


Learning to Paraphrase Sentences to Different Complexity Levels


Learning to Parse Wireframes in Images of Man-Made Environments


Learning to Perceive in Deep Model-Free Reinforcement Learning


Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models


Learning to Perform Local Rewriting for Combinatorial Optimization


Learning to Perform Role-Filler Binding with Schematic Knowledge


Learning to Perturb Word Embeddings for Out-of-distribution QA


Learning to Plan Hierarchically from Curriculum


Learning to Plan Maneuverable and Agile Flight Trajectory with Optimization Embedded Networks


Learning to Plan for Language Modeling from Unlabeled Data


Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs


Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees


Learning to Plan via Supervised Contrastive Learning and Strategic Interpolation: A Chess Case Study


Learning to Play Air Hockey with Model-Based Deep Reinforcement Learning


Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis


Learning to Play Cup-and-Ball with Noisy Camera Observations


Learning to Play Foosball: System and Baselines


Learning to Play General Video-Games via an Object Embedding Network


Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents


Learning to Play Imperfect-Information Games by Imitating an Oracle Planner


Learning to Play No-Press Diplomacy with Best Response Policy Iteration


Learning to Play Othello with Deep Neural Networks


Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition


Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning


Learning to Poison Large Language Models for Downstream Manipulation


Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints


Learning to Predict Concept Ordering for Common Sense Generation


Learning to Predict Gradients for Semi-Supervised Continual Learning


Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters


Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis


Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning


Learning to Predict Trustworthiness with Steep Slope Loss


Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels


Learning to Predict the Cosmological Structure Formation


Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction


Learning to Prevent Monocular SLAM Failure using Reinforcement Learning


Learning to Produce Semi-dense Correspondences for Visual Localization


Learning to Prompt Knowledge Transfer for Open-World Continual Learning


Learning to Propagate for Graph Meta-Learning


Learning to Prove Theorems by Learning to Generate Theorems


Learning to Prove Theorems via Interacting with Proof Assistants


Learning to Prune in Training via Dynamic Channel Propagation


Learning to Purify Noisy Labels via Meta Soft Label Corrector


Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities


Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain


Learning to Rank Patches for Unbiased Image Redundancy Reduction


Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs


Learning to Rank Rationales for Explainable Recommendation


Learning to Rank Using Localized Geometric Mean Metrics


Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting


Learning to Rank from Relevance Judgments Distributions


Learning to Rank in Generative Retrieval


Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models


Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation


Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction


Learning to Reason and Memorize with Self-Notes


Learning to Reason for Long-Form Story Generation


Learning to Reason for Text Generation from Scientific Tables


Learning to Reason from Feedback at Test-Time


Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters


Learning to Reason under Off-Policy Guidance


Learning to Reason via Mixture-of-Thought for Logical Reasoning


Learning to Reason via Program Generation, Emulation, and Search


Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures


Learning to Reason without External Rewards


Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting


Learning to Recognize Discontiguous Entities


Learning to Recombine and Resample Data for Compositional Generalization


Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild


Learning to Reconstruct 3D Manhattan Wireframes from a Single Image


Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image


Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise


Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing


Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations


Learning to Reconstruct People in Clothing from a Single RGB Camera


Learning to Reconstruct Signals From Binary Measurements


Learning to Reconstruct Texture-less Deformable Surfaces from a Single View


Learning to Recover Spectral Reflectance from RGB Images


Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation


Learning to Rectify for Robust Learning with Noisy Labels


Learning to Reduce Information Bottleneck for Object Detection in Aerial Images


Learning to Refine with Fine-Grained Natural Language Feedback


Learning to Refuse: Towards Mitigating Privacy Risks in LLMs


Learning to Regress Bodies from Images using Differentiable Semantic Rendering


Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation


Learning to Relate to Previous Turns in Conversational Search


Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval


Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting


Learning to Remove Cuts in Integer Linear Programming


Learning to Remove Wrinkled Transparent Film with Polarized Prior


Learning to Remove: Towards Isotropic Pre-trained BERT Embedding


Learning to Represent Bilingual Dictionaries


Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog


Learning to Retrieve Engaging Follow-Up Queries


Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems


Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering


Learning to Retrieve Videos by Asking Questions


Learning to Reuse Distractors to support Multiple Choice Question Generation in Education


Learning to Revise References for Faithful Summarization


Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming


Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike Streams


Learning to Route Among Specialized Experts for Zero-Shot Generalization


Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments


Learning to Run with Actor-Critic Ensemble


Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation


Learning to Sample Hard Instances for Graph Algorithms


Learning to Satisfy Unknown Constraints in Iterative MPC


Learning to Scaffold: Optimizing Model Explanations for Teaching


Learning to Scale Logits for Temperature-Conditional GFlowNets


Learning to Schedule Communication in Multi-agent Reinforcement Learning


Learning to Score Behaviors for Guided Policy Optimization


Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt


Learning to Search in Long Documents Using Document Structure


Learning to See Through Obstructions with Layered Decomposition


Learning to Segment Breast Biopsy Whole Slide Images


Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks


Learning to Segment Medical Images from Few-Shot Sparse Labels


Learning to Segment Rigid Motions from Two Frames


Learning to Segment from Noisy Annotations: A Spatial Correction Approach


Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation


Learning to Select Camera Views: Efficient Multiview Understanding at Few Glances


Learning to Select Pivotal Samples for Meta Re-weighting


Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling


Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints


Learning to Select from Multiple Options


Learning to Self-Train for Semi-Supervised Few-Shot Classification


Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes


Learning to Sequence and Blend Robot Skills via Differentiable Optimization


Learning to Share and Hide Intentions using Information Regularization


Learning to Simulate Aerosol Dynamics with Graph Neural Networks


Learning to Simulate Daily Activities via Modeling Dynamic Human Needs


Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization


Learning to Skip the Middle Layers of Transformers


Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration


Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer


Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process


Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning


Learning to Solve Routing Problems via Distributionally Robust Optimization


Learning to Solve Tasks with Exploring Prior Behaviours


Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum


Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention


Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding


Learning to Speak and Act in a Fantasy Text Adventure Game


Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining


Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration


Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces


Learning to Steer Markovian Agents under Model Uncertainty


Learning to Structure an Image with Few Colors


Learning to Substitute Ingredients in Recipes


Learning to Substitute Spans towards Improving Compositional Generalization


Learning to Substitute Words with Model-based Score Ranking


Learning to Super Resolve Intensity Images from Events


Learning to Super-Resolve Blurry Images with Events


Learning to Take Directions One Step at a Time


Learning to Taste: A Multimodal Wine Dataset


Learning to Terminate in Object Navigation


Learning to Track Objects from Unlabeled Videos


Learning to Transfer Prompts for Text Generation


Learning to Transfer Texture from Clothing Images to 3D Humans


Learning to Transfer for Traffic Forecasting via Multi-task Learning


Learning to Transfer: Unsupervised Meta Domain Translation


Learning to Transform Dynamically for Better Adversarial Transferability


Learning to Transform for Generalizable Instance-wise Invariance


Learning to Transport with Neural Networks


Learning to Traverse Latent Spaces for Musical Score Inpainting


Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL


Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation


Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers


Learning to Untangle Genome Assembly with Graph Convolutional Networks


Learning to Update Natural Language Comments Based on Code Changes


Learning to Upsample by Learning to Sample


Learning to Use Chopsticks in Diverse Gripping Styles


Learning to Verify Summary Facts with Fine-Grained LLM Feedback


Learning to Visually Localize Sound Sources from Mixtures without Prior Source Knowledge


Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models


Learning to Warm-Start Fixed-Point Optimization Algorithms


Learning to Watermark LLM-generated Text via Reinforcement Learning


Learning to Weight for Text Classification


Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training


Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks


Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning


Learning to adapt class-specific features across domains for semantic segmentation


Learning to adapt: a meta-learning approach for speaker adaptation


Learning to automate cryo-electron microscopy data collection with Ptolemy


Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making


Learning to be a Statistician: Learned Estimator for Number of Distinct Values


Learning to combine Grammatical Error Corrections


Learning to communicate about shared procedural abstractions


Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio


Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search


Learning to detect RFI in radio astronomy without seeing it


Learning to detect an animal sound from five examples


Learning to diagnose from scratch by exploiting dependencies among labels


Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences


Learning to do multiframe wavefront sensing unsupervisedly: applications to blind deconvolution


Learning to engineer protein flexibility


Learning to forecast: The probabilistic time series forecasting challenge


Learning to generalize Dispatching rules on the Job Shop Scheduling


Learning to generate and corr- uh I mean repair language in real-time


Learning to generate feasible graphs using graph grammars


Learning to generate line drawings that convey geometry and semantics


Learning to generate one-sentence biographies from Wikidata


Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks


Learning to ignore: rethinking attention in CNNs


Learning to infer in recurrent biological networks


Learning to learn ecosystems from limited data -- a meta-learning approach


Learning to learn with an evolutionary strategy applied to variational quantum algorithms


Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task


Learning to match transient sound events using attentional similarity for few-shot sound recognition


Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation


Learning to optimize with convergence guarantees using nonlinear system theory


Learning to predict arbitrary quantum processes


Learning to predict synchronization of coupled oscillators on randomly generated graphs


Learning to recognize occluded and small objects with partial inputs


Learning to refer informatively by amortizing pragmatic reasoning


Learning to regularize with a variational autoencoder for hydrologic inverse analysis


Learning to restore images degraded by atmospheric turbulence using uncertainty


Learning to score the figure skating sports videos


Learning to search efficiently for causally near-optimal treatments


Learning to select data for transfer learning with Bayesian Optimization


Learning to simulate and design for structural engineering


Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides


Learning to solve complex tasks by growing knowledge culturally across generations


Learning to solve geometric construction problems from images


Learning to solve the credit assignment problem


Learning to synthesise the ageing brain without longitudinal data


Learning to walk in confined spaces using 3D representation


Learning topological states from randomized measurements using variational tensor network tomography


Learning truly monotone operators with applications to nonlinear inverse problems


Learning under Imitative Strategic Behavior with Unforeseeable Outcomes


Learning under Misspecified Objective Spaces


Learning under Model Misspecification: Applications to Variational and Ensemble methods


Learning unfolded networks with a cyclic group structure


Learning unknown ODE models with Gaussian processes


Learning variational autoencoders via MCMC speed measures


Learning visual-based deformable object rearrangement with local graph neural networks


Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs


Learning with 3D rotations, a hitchhiker's guide to SO(3)


Learning with Algorithmic Supervision via Continuous Relaxations


Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition


Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical


Learning with Confidence: Training Better Classifiers from Soft Labels


Learning with Expert Abstractions for Efficient Multi-Task Continuous Control


Learning with Explicit Shape Priors for Medical Image Segmentation


Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning


Learning with Instance-Dependent Label Noise: A Sample Sieve Approach


Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality


Learning with Logical Constraints but without Shortcut Satisfaction


Learning with Mixture of Prototypes for Out-of-Distribution Detection


Learning with Multiplicative Perturbations


Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision


Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal


Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection


Learning with Noisy Labels via Sparse Regularization


Learning with Noisy labels via Self-supervised Adversarial Noisy Masking


Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space


Learning with Out-of-Distribution Data for Audio Classification


Learning with Partial Labels from Semi-supervised Perspective


Learning with Posterior Sampling for Revenue Management under Time-varying Demand


Learning with Rejection for Abstractive Text Summarization


Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions


Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning


Learning with Unmasked Tokens Drives Stronger Vision Learners


Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with Relative Geometric Consistency


Learning with minibatch Wasserstein : asymptotic and gradient properties


Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation


Learning with privileged information via adversarial discriminative modality distillation


Learning with symmetric positive definite matrices via generalized Bures-Wasserstein geometry


Learning without Forgetting for 3D Point Cloud Objects


Learning without Isolation: Pathway Protection for Continual Learning


Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks


Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments


Learning-Assisted Optimization for Transmission Switching


Learning-Augmented Algorithms for Online Steiner Tree


Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds


Learning-Augmented Private Algorithms for Multiple Quantile Release


Learning-Based Animation of Clothing for Virtual Try-On


Learning-Based Conformal Tube MPC for Safe Control in Interactive Multi-Agent Systems


Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks


Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors


Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems


Learning-Based Efficient Approximation of Data-Enabled Predictive Control


Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection


Learning-Based Image Compression for Machines


Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection


Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees


Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission


Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute


Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States


Learning-Based Passive Fault-Tolerant Control of a Quadrotor with Rotor Failure


Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information


Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model


Learning-Based Proxy Collision Detection for Robot Motion Planning Applications


Learning-Based Reconstruction of FRI Signals


Learning-Based Stable Optimal Control for Infinite-Time Nonlinear Regulation Problems


Learning-Driven Exploration for Reinforcement Learning


Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling


Learning-Rate-Free Learning by D-Adaptation


Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds


Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine


Learning-augmented Online Algorithm for Two-level Ski-rental Problem


Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments


Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems


Learning-based Defect Recognition for Quasi-Periodic Microscope Images


Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching


Learning-based Localizability Estimation for Robust LiDAR Localization


Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping


Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers


Learning-based Observer for Coupled Disturbance


Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control


Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth


Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPs


Learning-based State Reconstruction for a Scalar Hyperbolic PDE under noisy Lagrangian Sensing


Learning-based model augmentation with LFRs


Learning-based pose edition for efficient and interactive design


Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imaging


Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension


Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching


Learning-to-Learn Stochastic Gradient Descent with Biased Regularization


Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity


LearningMatch: Siamese Neural Network Learns the Match Manifold


Learnings Options End-to-End for Continuous Action Tasks


Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi


Learnware of Language Models: Specialized Small Language Models Can Do Big


Leasing the Cloud-Edge Continuum, à la Carte


Least Redundant Gated Recurrent Neural Network


Least Squares Estimation Using Sketched Data with Heteroskedastic Errors


Least Squares Normalized Cross Correlation


Least Squares estimation of two ordered monotone regression curves


Least squares surface reconstruction on arbitrary domains


Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA


Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation


Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation


Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients


Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users


Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection


Leave-One-Out Stable Conformal Prediction


Leaving Reality to Imagination: Robust Classification via Generated Datasets


Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers


LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning


Left Ringing: Betelgeuse Illuminates the Connection Between Convective outbursts, Mode switching, and Mass Ejection in Red Supergiants


Left ventricle quantification through spatio-temporal CNNs


Left/Right Brain, human motor control and the implications for robotics


Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models


Legacy Archive for Microwave Background Data Analysis (LAMBDA): An Overview


Legacy Photo Editing with Learned Noise Prior


Legal Case Document Similarity: You Need Both Network and Text


Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model


Legal Extractive Summarization of U.S. Court Opinions


Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction


Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting


Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts


Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction


Legal and Political Stance Detection of SCOTUS Language


LegalAgentBench: Evaluating LLM Agents in Legal Domain


LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain


LegalCore: A Dataset for Legal Documents Event Coreference Resolution


LegalDuet: Learning Fine-grained Representations for Legal Judgment Prediction via a Dual-View Contrastive Learning


LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text


LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text


LegalNLP -- Natural Language Processing methods for the Brazilian Legal Language


Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets


Leggiero: Analog WiFi Backscatter with Payload Transparency


Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions


Legilimens: Practical and Unified Content Moderation for Large Language Model Services


Legio: Fault Resiliency for Embarrassingly Parallel MPI Applications


Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Training


Legislative effectiveness hangs in the balance: Studying balance and polarization through partitioning signed networks


Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams


Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation


LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction


LegoPET: Hierarchical Feature Guided Conditional Diffusion for PET Image Reconstruction


Lemmas: Generation, Selection, Application


Lemotif: An Affective Visual Journal Using Deep Neural Networks


Lemur: Harmonizing Natural Language and Code for Language Agents


Lemur: Integrating Large Language Models in Automated Program Verification


LenAtten: An Effective Length Controlling Unit For Text Summarization


LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images


Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free Communication


Lending Orientation to Neural Networks for Cross-view Geo-localization


Length Generalization of Causal Transformers without Position Encoding


Length Optimization in Conformal Prediction


Length Scale Control in Topology Optimization using Fourier Enhanced Neural Networks


Length is a Curse and a Blessing for Document-level Semantics


Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search


Length-Aware DETR for Robust Moment Retrieval


Length-Aware Motion Synthesis via Latent Diffusion


Length-Aware Multi-Kernel Transformer for Long Document Classification


Length-Controlled Margin-Based Preference Optimization without Reference Model


Lenient Regret and Good-Action Identification in Gaussian Process Bandits


Lenna: Language Enhanced Reasoning Detection Assistant


Lens Distortion Rectification using Triangulation based Interpolation


Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation


Lens functions for exploring UMAP Projections with Domain Knowledge


LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos


LensLeech: On-Lens Interaction for Arbitrary Camera Devices


LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction


Lensing Without Borders. I. A Blind Comparison of the Amplitude of Galaxy-Galaxy Lensing Between Independent Imaging Surveys


Lensing bias on cosmological parameters from bright standard sirens


LensingETC: a tool to optimize multi-filter imaging campaigns of galaxy-scale strong lensing systems


LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification


Leolani: a reference machine with a theory of mind for social communication


Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks


Lepard: Learning partial point cloud matching in rigid and deformable scenes


Lepto-Hadronic Scenarios for TeV Extensions of Gamma-Ray Burst Afterglow Spectra


Leptohadronic Multimessenger Modeling of 324 Gamma-Ray Blazars


Lepton Flavor Asymmetries: from the early Universe to BBN


Lepton-Flavor-Violating ALP Signals with TeV-Scale Muon Beams


Leptophobic $Z^\prime$ bosons in the secluded UMSSM


Leptophobic dark photon interpretation of the $η^{\left(\prime\right)}\rightarrowπ^0\left(η\right)γγ$ puzzle


Leray-Schauder Mappings for Operator Learning


Lero: A Learning-to-Rank Query Optimizer


Leroy and Blazy were right: their memory model soundness proof is automatable (Extended Version)


Lesion Elevation Prediction from Skin Images Improves Diagnosis


Lesion Guided Explainable Few Weak-shot Medical Report Generation


Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale


Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis


Lesion-aware Dynamic Kernel for Polyp Segmentation


Lesion-aware network for diabetic retinopathy diagnosis


LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation


Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs


Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models


Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems


Less Is Better: Unweighted Data Subsampling via Influence Function


Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation


Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation


Less Manual Work for Safety Engineers: Towards an Automated Safety Reasoning with Safety Patterns


Less Peaky and More Accurate CTC Forced Alignment by Label Priors


Less Power for More Learning: Restricting OCaml Features for Effective Teaching


Less Quantum, More Advantage: An End-to-End Quantum Algorithm for the Jones Polynomial


Less but Better: Enabling Generalized Zero-shot Learning Towards Unseen Domains by Intrinsic Learning from Redundant LLM Semantics


Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin


Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Models


Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models


Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs


Less is More: Adaptive Program Repair with Bug Localization and Preference Learning


Less is More: Data-Efficient Complex Question Answering over Knowledge Bases


Less is More: Discovering Concise Network Explanations


Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection


Less is More: Facial Landmarks can Recognize a Spontaneous Smile


Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits


Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification


Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA


Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models


Less is More: Minimizing Code Reorganization using XTREE


Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery


Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective


Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning


Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies


Less is More: Proxy Datasets in NAS approaches


Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation


Less is More: Rejecting Unreliable Reviews for Product Question Answering


Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness


Less is More: Revisiting the Gaussian Mechanism for Differential Privacy


Less is More: Reweighting Important Spectral Graph Features for Recommendation


Less is More: Selective Layer Finetuning with SubTuning


Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance


Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network


Less is More: Simplifying Network Traffic Classification Leveraging RFCs


Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining


Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality


Less is More: Summary of Long Instructions is Better for Program Synthesis


Less is More: Surgical Phase Recognition from Timestamp Supervision


Less is More: Task-aware Layer-wise Distillation for Language Model Compression


Less is More: Token Context-aware Learning for Object Tracking


Less is More: on the Over-Globalizing Problem in Graph Transformers


Less is more: Embracing sparsity and interpolation with Esiformer for time series forecasting


Less is more: Faster and better music version identification with embedding distillation


Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning


Less is more: concatenating videos for Sign Language Translation from a small set of signs


Less than One-shot: Named Entity Recognition via Extremely Weak Supervision


Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics


Less-to-More Generalization: Unlocking More Controllability by In-Context Generation


LessonPlanner: Assisting Novice Teachers to Prepare Pedagogy-Driven Lesson Plans with Large Language Models


Lessons Learned from a Citizen Science Project for Natural Language Processing


Lessons Learned from the Training of GANs on Artificial Datasets


Lessons Learned from the URGENT 2024 Speech Enhancement Challenge


Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions


Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!


Lessons learned developing and using a machine learning model to automatically transcribe 2.3 million handwritten occupation codes


Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review


Lessons learned from hyper-parameter tuning for microservice candidate identification


Lessons learned from the evaluation of Spanish Language Models


Lester: rotoscope animation through video object segmentation and tracking


Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation


Let AI Read First: Enhancing Reading Abilities for Individuals with Dyslexia through Artificial Intelligence


Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval


Let Androids Dream of Electric Sheep: A Human-like Image Implication Understanding and Reasoning Framework


Let Each Quantum Bit Choose Its Basis Gates


Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning


Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology


Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis


Let Invariant Rationale Discovery Inspire Graph Contrastive Learning


Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark


Let Me DeCode You: Decoder Conditioning with Tabular Data


Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model


Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models


Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones


Let Network Decide What to Learn: Symbolic Music Understanding Model Based on Large-scale Adversarial Pre-training


Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows


Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models


Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals


Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model


Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models


Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions


Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence


Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks


Let the Poem Hit the Rhythm: Using a Byte-Based Transformer for Beat-Aligned Poetry Generation


Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components


Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning


Let's Agree to Agree: Targeting Consensus for Incomplete Preferences through Majority Dynamics


Let's Ask Again: Refine Network for Automatic Question Generation


Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images


Let's FACE it. Finnish Poetry Generation with Aesthetics and Framing


Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings


Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text Recognition


Let's Gamble: How a Poor Visualization Can Elicit Risky Behavior


Let's Get It Started: Fostering the Discoverability of New Releases on Deezer


Let's Get Vysical: Perceptual Accuracy In Visual and Tactile Encodings


Let's Give a Voice to Conversational Agents in Virtual Reality


Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning


Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning


Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence


Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments


Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses


Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games


Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models


Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs


Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models


Let's Think Dot by Dot: Hidden Computation in Transformer Language Models


Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought


Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation


Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications


Let's Verify Math Questions Step by Step


Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud


Let's do the time-warp-attend: Learning topological invariants of dynamical systems


Let's move on: Topic Change in Robot-Facilitated Group Discussions


Let's practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments


Let's shake on it: Extracting secure shared keys from Wi-Fi CSI


Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language


Lethean Attack: An Online Data Poisoning Technique


Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings


Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books


LettuceDetect: A Hallucination Detection Framework for RAG Applications


Level Assembly as a Markov Decision Process


Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution


Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment


Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection


Level Up: Private Non-Interactive Decision Tree Evaluation using Levelled Homomorphic Encryption


Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation


Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation


Level set-based inverse homogenisation of three-dimensional piezoelectric materials


Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning


Level-S$^2$fM: Structure from Motion on Neural Level Set of Implicit Surfaces


Level-line Guided Edge Drawing for Robust Line Segment Detection


Level-p-complexity of Boolean Functions using Thinning, Memoization, and Polynomials


LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers


Levenshtein Training for Word-level Quality Estimation


Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations


Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks


Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy


Leverage classifier: Another look at support vector machine


Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust


Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction


Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?


Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging


Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment


Leveraging ASIC AI Chips for Homomorphic Encryption


Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data


Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy


Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding


Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment


Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection


Leveraging Application Data Constraints to Optimize Database-Backed Web Applications


Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity


Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy


Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation


Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation


Leveraging BEV Representation for 360-degree Visual Place Recognition


Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech Recognition and Subtitling


Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation


Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation


Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study


Leveraging Class Hierarchies with Metric-Guided Prototype Learning


Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration


Leveraging Code to Improve In-context Learning for Semantic Parsing


Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification


Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction


Leveraging Compositional Methods for Modeling and Verification of an Autonomous Taxi System


Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry


Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning


Leveraging Content and Acoustic Representations for Speech Emotion Recognition


Leveraging Content and Context Cues for Low-Light Image Enhancement


Leveraging Context for Multimodal Fallacy Classification in Political Debates


Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments


Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples


Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order


Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems


Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification


Leveraging Data Augmentation for Process Information Extraction


Leveraging Data Geometry to Mitigate CSM in Steganalysis


Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars


Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality


Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization


Leveraging Demonstrations with Latent Space Priors


Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks


Leveraging Depth Maps and Attention Mechanisms for Enhanced Image Inpainting


Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation


Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision


Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data


Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks


Leveraging Discourse Information Effectively for Authorship Attribution


Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low Data


Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster


Leveraging Diverse Semantic-based Audio Pretrained Models for Singing Voice Conversion


Leveraging Domain Adaptation for Accurate Machine Learning Predictions of New Halide Perovskites


Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization


Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification


Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts


Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration


Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction


Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias


Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution


Leveraging Entailment Judgements in Cross-Lingual Summarisation


Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization


Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models


Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking


Leveraging Event Specific and Chunk Span features to Extract COVID Events from tweets


Leveraging Experience in Lifelong Multi-Agent Pathfinding