• [ 64766 ] Transfer Learning in Information Criteria-based Feature Selection
• [ 64768 ] tntorch: Tensor Network Learning with PyTorch
• [ 64769 ] InterpretDL: Explaining Deep Models in PaddlePaddle
• [ 64774 ] [Re] Value Alignment Verification

Q&A on RocketChat immediately following Lightning Talks

• [ 64787 ] OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
• [ 64788 ] Predictive Coding beyond Gaussian Distributions
• [ 64790 ] What Makes Graph Neural Networks Miscalibrated?
• [ 64791 ] NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
• [ 64792 ] Redundancy-Free Message Passing for Graph Neural Networks
• [ 64794 ] EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
• [ 64796 ] Probing Classifiers are Unreliable for Concept Removal and Detection

Q&A on RocketChat immediately following Lightning Talks

• [ 64777 ] [Re] Projection-based Algorithm for Updating the TruncatedSVD of Evolving Matrices
• [ 64780 ] [Re] Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

Q&A on RocketChat immediately following Lightning Talks

• [ 64803 ] Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
• [ 64804 ] Non-Gaussian Tensor Programs
• [ 64808 ] Chromatic Correlation Clustering, Revisited

Q&A on RocketChat immediately following Lightning Talks

• [ 64834 ] Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
• [ 64835 ] Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
• [ 64836 ] An Investigation into Whitening Loss for Self-supervised Learning
• [ 64837 ] Task-Free Continual Learning via Online Discrepancy Distance Learning
• [ 64838 ] Efficient Knowledge Distillation from Model Checkpoints
• [ 64839 ] Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
• [ 64841 ] Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics

Q&A on RocketChat immediately following Lightning Talks

• [ 64809 ] Active Bayesian Causal Inference
• [ 64810 ] Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
• [ 64811 ] Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
• [ 64812 ] Provable Benefit of Multitask Representation Learning in Reinforcement Learning
• [ 64813 ] Self-Supervised Learning via Maximum Entropy Coding
• [ 64816 ] Counterfactual Temporal Point Processes
• [ 64817 ] Provable Subspace Identification Under Post-Nonlinear Mixtures
• [ 64818 ] VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?
• [ 64819 ] Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching

Q&A on RocketChat immediately following Lightning Talks

• [ 64820 ] CoPur: Certifiably Robust Collaborative Inference via Feature Purification
• [ 64821 ] Data Augmentation MCMC for Bayesian Inference from Privatized Data
• [ 64822 ] Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
• [ 64824 ] On the Learning Mechanisms in Physical Reasoning
• [ 64825 ] Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
• [ 64827 ] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
• [ 64828 ] Conformal Off-Policy Prediction in Contextual Bandits
• [ 64830 ] A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning

Q&A on RocketChat immediately following Lightning Talks

• [ 64842 ] Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
• [ 64843 ] Improved Fine-Tuning by Better Leveraging Pre-Training Data
• [ 64845 ] Task Discovery: Finding the Tasks that Neural Networks Generalize on
• [ 64846 ] Coresets for Relational Data and The Applications
• [ 64847 ] Teacher Forcing Recovers Reward Functions for Text Generation
• [ 64849 ] UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
• [ 64850 ] Gradient Estimation with Discrete Stein Operators
• [ 64851 ] Detecting Abrupt Changes in Sequential Pairwise Comparison Data
• [ 64852 ] Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis

Q&A on RocketChat immediately following Lightning Talks

• [ 64876 ] Cross Aggregation Transformer for Image Restoration
• [ 64877 ] BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
• [ 64878 ] Inception Transformer
• [ 64879 ] GhostNetV2: Enhance Cheap Operation with Long-Range Attention
• [ 64880 ] MCMAE: Masked Convolution Meets Masked Autoencoders
• [ 64881 ] Deep Fourier Up-Sampling
• [ 64883 ] SKFlow: Learning Optical Flow with Super Kernels
• [ 64885 ] RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Q&A on RocketChat immediately following Lightning Talks

• [ 64853 ] Benefits of Additive Noise in Composing Classes with Bounded Capacity
• [ 64857 ] Posterior Matching for Arbitrary Conditioning
• [ 64858 ] "Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
• [ 64859 ] Lipschitz Bandits with Batched Feedback
• [ 64860 ] Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
• [ 64861 ] Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
• [ 64862 ] Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization
• [ 64863 ] Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution

Q&A on RocketChat immediately following Lightning Talks

• [ 64865 ] Robust Learning against Relational Adversaries
• [ 64867 ] SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training
• [ 64869 ] Trustworthy Monte Carlo
• [ 64870 ] Sampling from Log-Concave Distributions with Infinity-Distance Guarantees
• [ 64871 ] Can Adversarial Training Be Manipulated By Non-Robust Features?
• [ 64873 ] Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning

Q&A on RocketChat immediately following Lightning Talks

• [ 64887 ] ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition
• [ 64888 ] GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
• [ 64890 ] One Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration
• [ 64891 ] Log-Polar Space Convolution Layers
• [ 64892 ] Streaming Radiance Fields for 3D Video Synthesis
• [ 64893 ] Flexible Neural Image Compression via Code Editing
• [ 64894 ] AutoST: Towards the Universal Modeling of Spatio-temporal Sequences

Q&A on RocketChat immediately following Lightning Talks

• [ 64897 ] Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
• [ 64899 ] NeMF: Neural Motion Fields for Kinematic Animation
• [ 64901 ] Brain Network Transformer
• [ 64902 ] Distilling Representations from GAN Generator via Squeeze and Span
• [ 64904 ] Graph Neural Networks with Adaptive Readouts
• [ 64906 ] Could Giant Pre-trained Image Models Extract Universal Representations?
• [ 64907 ] On the relationship between variational inference and auto-associative memory

Q&A on RocketChat immediately following Lightning Talks

• [ 64921 ] Understanding Square Loss in Training Overparametrized Neural Network Classifiers
• [ 64922 ] UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
• [ 64923 ] LOG: Active Model Adaptation for Label-Efficient OOD Generalization
• [ 64927 ] MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
• [ 64929 ] Distributionally Robust Optimization with Data Geometry

Q&A on RocketChat immediately following Lightning Talks

• [ 64908 ] Diagonal State Spaces are as Effective as Structured State Spaces
• [ 64911 ] You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection
• [ 64913 ] Wasserstein Iterative Networks for Barycenter Estimation
• [ 64914 ] Neural Approximation of Graph Topological Features
• [ 64915 ] Regularized Molecular Conformation Fields
• [ 64916 ] Is a Modular Architecture Enough?
• [ 64917 ] Knowledge-Aware Bayesian Deep Topic Model
• [ 64918 ] Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Q&A on RocketChat immediately following Lightning Talks

• [ 64932 ] Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning
• [ 64933 ] Estimating graphical models for count data with applications to single-cell gene network
• [ 64934 ] Concentration of Data Encoding in Parameterized Quantum Circuits
• [ 64935 ] On Learning Fairness and Accuracy on Multiple Subgroups
• [ 64936 ] Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
• [ 64938 ] Doubly Robust Counterfactual Classification
• [ 64939 ] Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders

Q&A on RocketChat immediately following Lightning Talks

• [ 64963 ] Conditional Meta-Learning of Linear Representations
• [ 64965 ] Supervising the Multi-Fidelity Race of Hyperparameter Configurations
• [ 64966 ] Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
• [ 64968 ] Deep Combinatorial Aggregation
• [ 64969 ] When to Update Your Model: Constrained Model-based Reinforcement Learning
• [ 64970 ] HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
• [ 64972 ] Multi-Sample Training for Neural Image Compression

Q&A on RocketChat immediately following Lightning Talks

• [ 64941 ] Collaborative Learning by Detecting Collaboration Partners
• [ 64942 ] Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
• [ 64944 ] Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class
• [ 64946 ] LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank
• [ 64947 ] Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
• [ 64949 ] Unified Optimal Transport Framework for Universal Domain Adaptation
• [ 64951 ] Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness

Q&A on RocketChat immediately following Lightning Talks

• [ 64952 ] Online Neural Sequence Detection with Hierarchical Dirichlet Point Process
• [ 64953 ] Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning
• [ 64954 ] A2: Efficient Automated Attacker for Boosting Adversarial Training
• [ 64955 ] Versatile Multi-stage Graph Neural Network for Circuit Representation
• [ 64956 ] Product Ranking for Revenue Maximization with Multiple Purchases
• [ 64957 ] Robust Semi-Supervised Learning when Not All Classes have Labels
• [ 64958 ] Meta-Complementing the Semantics of Short Texts in Neural Topic Models
• [ 64959 ] Byzantine-tolerant federated Gaussian process regression for streaming data

Q&A on RocketChat immediately following Lightning Talks

• [ 64973 ] Provable Generalization of Overparameterized Meta-learning Trained with SGD
• [ 64974 ] Elucidating the Design Space of Diffusion-Based Generative Models
• [ 64975 ] HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
• [ 64977 ] Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
• [ 64978 ] Fast Instrument Learning with Faster Rates
• [ 64980 ] Multi-view Subspace Clustering on Topological Manifold
• [ 64981 ] Distributional Reinforcement Learning for Risk-Sensitive Policies

Q&A on RocketChat immediately following Lightning Talks

• [ 65007 ] Contrastive Graph Structure Learning via Information Bottleneck for Recommendation
• [ 65008 ] Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective
• [ 65009 ] Revisiting Heterophily For Graph Neural Networks
• [ 65010 ] Understanding the Failure of Batch Normalization for Transformers in NLP
• [ 65011 ] A Unified Model for Multi-class Anomaly Detection
• [ 65013 ] A Closer Look at Offline RL Agents
• [ 65014 ] Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

Q&A on RocketChat immediately following Lightning Talks

• [ 64984 ] PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
• [ 64985 ] Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
• [ 64986 ] Hand-Object Interaction Image Generation
• [ 64988 ] Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations
• [ 64989 ] Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis
• [ 64991 ] Geometry-aware Two-scale PIFu Representation for Human Reconstruction

Q&A on RocketChat immediately following Lightning Talks

• [ 64995 ] ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis
• [ 64996 ] Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
• [ 64997 ] Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
• [ 64999 ] Conditional Diffusion Process for Inverse Halftoning
• [ 65000 ] Object-Category Aware Reinforcement Learning
• [ 65001 ] Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

Q&A on RocketChat immediately following Lightning Talks

• [ 65015 ] Get More at Once: Alternating Sparse Training with Gradient Correction
• [ 65016 ] Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels
• [ 65019 ] Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
• [ 65020 ] MExMI: Pool-based Active Model Extraction Crossover Membership Inference
• [ 65021 ] Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
• [ 65022 ] Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
• [ 65023 ] Optimal Positive Generation via Latent Transformation for Contrastive Learning
• [ 65025 ] Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Q&A on RocketChat immediately following Lightning Talks

• [ 65048 ] Autoinverse: Uncertainty Aware Inversion of Neural Networks
• [ 65049 ] Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
• [ 65050 ] Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning
• [ 65051 ] PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery
• [ 65052 ] Inverse Design for Fluid-Structure Interactions using Graph Network Simulators
• [ 65054 ] Learning Physical Dynamics with Subequivariant Graph Neural Networks
• [ 65055 ] PALBERT: Teaching ALBERT to Ponder
• [ 65056 ] Towards Practical Control of Singular Values of Convolutional Layers
• [ 65057 ] Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
• [ 65058 ] TA-GATES: An Encoding Scheme for Neural Network Architectures

Q&A on RocketChat immediately following Lightning Talks

• [ 65026 ] Online Frank-Wolfe with Arbitrary Delays
• [ 65028 ] Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms
• [ 65030 ] Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox
• [ 65031 ] Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
• [ 65034 ] Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model
• [ 65035 ] Coresets for Wasserstein Distributionally Robust Optimization Problems
• [ 65036 ] Exploitability Minimization in Games and Beyond

Q&A on RocketChat immediately following Lightning Talks

• [ 65038 ] Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
• [ 65039 ] Exact Shape Correspondence via 2D graph convolution
• [ 65040 ] Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
• [ 65041 ] Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning
• [ 65042 ] Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
• [ 65043 ] Distributed Online Convex Optimization with Compressed Communication
• [ 65044 ] Composition Theorems for Interactive Differential Privacy
• [ 65045 ] Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
• [ 65046 ] Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients

Q&A on RocketChat immediately following Lightning Talks

• [ 65060 ] LieGG: Studying Learned Lie Group Generators
• [ 65062 ] Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality
• [ 65066 ] Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

Q&A on RocketChat immediately following Lightning Talks

• [ 65070 ] GAGA: Deciphering Age-path of Generalized Self-paced Regularizer
• [ 65071 ] S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning
• [ 65072 ] Differentiable hierarchical and surrogate gradient search for spiking neural networks
• [ 65075 ] Online Training Through Time for Spiking Neural Networks
• [ 65076 ] DivBO: Diversity-aware CASH for Ensemble Learning
• [ 65079 ] Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
• [ 65080 ] Does Momentum Change the Implicit Regularization on Separable Data?

Q&A on RocketChat immediately following Lightning Talks

• [ 65092 ] Contact-aware Human Motion Forecasting
• [ 65093 ] Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
• [ 65094 ] Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
• [ 65096 ] SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
• [ 65097 ] CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation
• [ 65098 ] AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
• [ 65099 ] TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
• [ 65100 ] Forecasting Human Trajectory from Scene History
• [ 65101 ] Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
• [ 65102 ] Language Conditioned Spatial Relation Reasoning for 3D Object Grounding

Q&A on RocketChat immediately following Lightning Talks

• [ 65103 ] Audio-Driven Co-Speech Gesture Video Generation
• [ 65104 ] Learning Equivariant Segmentation with Instance-Unique Querying
• [ 65105 ] Grounded Video Situation Recognition
• [ 65106 ] Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
• [ 65108 ] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
• [ 65109 ] BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
• [ 65112 ] Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
• [ 65113 ] APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking

Q&A on RocketChat immediately following Lightning Talks

• [ 65081 ] Provable General Function Class Representation Learning in Multitask Bandits and MDP
• [ 65082 ] Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
• [ 65083 ] Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
• [ 65088 ] Toward Robust Spiking Neural Network Against Adversarial Perturbation
• [ 65089 ] The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
• [ 65090 ] Fuzzy Learning Machine
• [ 65091 ] BLOX: Macro Neural Architecture Search Benchmark and Algorithms

Q&A on RocketChat immediately following Lightning Talks

• [ 65136 ] EpiGRAF: Rethinking training of 3D GANs
• [ 65137 ] CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
• [ 65138 ] HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
• [ 65139 ] Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
• [ 65140 ] Residual Multiplicative Filter Networks for Multiscale Reconstruction
• [ 65141 ] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
• [ 65142 ] LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness
• [ 65143 ] Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Q&A on RocketChat immediately following Lightning Talks

• [ 65116 ] DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
• [ 65117 ] Explicable Policy Search
• [ 65120 ] CUP: Critic-Guided Policy Reuse
• [ 65121 ] TarGF: Learning Target Gradient Field for Object Rearrangement
• [ 65122 ] RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
• [ 65123 ] When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

Q&A on RocketChat immediately following Lightning Talks

• [ 65147 ] The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
• [ 65148 ] LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
• [ 65149 ] Your Out-of-Distribution Detection Method is Not Robust!
• [ 65151 ] SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
• [ 65152 ] Accelerating Certified Robustness Training via Knowledge Transfer
• [ 65155 ] Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

Q&A on RocketChat immediately following Lightning Talks

• [ 65125 ] Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
• [ 65126 ] Learning to Constrain Policy Optimization with Virtual Trust Region
• [ 65127 ] Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
• [ 65128 ] Heatmap Distribution Matching for Human Pose Estimation
• [ 65129 ] Explainable Reinforcement Learning via Model Transforms
• [ 65131 ] Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
• [ 65132 ] Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
• [ 65133 ] Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes
• [ 65135 ] MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

Q&A on RocketChat immediately following Lightning Talks

• [ 65180 ] Watermarking for Out-of-distribution Detection
• [ 65181 ] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
• [ 65183 ] Self-supervised Amodal Video Object Segmentation
• [ 65184 ] Spectrum Random Masking for Generalization in Image-based Reinforcement Learning
• [ 65185 ] Learning Substructure Invariance for Out-of-Distribution Molecular Representations
• [ 65187 ] Stochastic Window Transformer for Image Restoration
• [ 65188 ] Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
• [ 65189 ] Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
• [ 65190 ] AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos

Q&A on RocketChat immediately following Lightning Talks

• [ 65158 ] E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
• [ 65159 ] Self-Organized Group for Cooperative Multi-agent Reinforcement Learning
• [ 65160 ] GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
• [ 65162 ] Multiagent Q-learning with Sub-Team Coordination
• [ 65163 ] Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
• [ 65164 ] Multi-agent Dynamic Algorithm Configuration
• [ 65165 ] Iso-Dream: Isolating Noncontrollable Visual Dynamics in World Models
• [ 65166 ] Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
• [ 65167 ] Learning Active Camera for Multi-Object Navigation

Q&A on RocketChat immediately following Lightning Talks

• [ 65169 ] Towards Versatile Embodied Navigation
• [ 65172 ] Equivariant Graph Hierarchy-Based Neural Networks
• [ 65173 ] Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
• [ 65174 ] Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval
• [ 65175 ] Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
• [ 65176 ] SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training
• [ 65177 ] Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking
• [ 65178 ] How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents
• [ 65179 ] A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks

Q&A on RocketChat immediately following Lightning Talks

• [ 65193 ] Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples
• [ 65195 ] Visual Concepts Tokenization
• [ 65196 ] DARE: Disentanglement-Augmented Rationale Extraction
• [ 65197 ] HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
• [ 65198 ] ZIN: When and How to Learn Invariance Without Environment Partition?
• [ 65199 ] DeepInteraction: 3D Object Detection via Modality Interaction
• [ 65200 ] Discovering Design Concepts for CAD Sketches

Q&A on RocketChat immediately following Lightning Talks

• [ 65224 ] A Spectral Approach to Item Response Theory
• [ 65225 ] Stability Analysis and Generalization Bounds of Adversarial Training
• [ 65226 ] Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
• [ 65228 ] Adam Can Converge Without Any Modification On Update Rules
• [ 65229 ] Poisson Flow Generative Models
• [ 65231 ] Contextual Bandits with Knapsacks for a Conversion Model
• [ 65232 ] Robust Graph Structure Learning over Images via Multiple Statistical Tests

Q&A on RocketChat immediately following Lightning Talks

• [ 65202 ] DropCov: A Simple yet Effective Method for Improving Deep Architectures
• [ 65203 ] A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
• [ 65204 ] Decoupling Features in Hierarchical Propagation for Video Object Segmentation
• [ 65205 ] BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling
• [ 65207 ] Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
• [ 65209 ] P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
• [ 65210 ] Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
• [ 65211 ] Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
• [ 65212 ] Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

Q&A on RocketChat immediately following Lightning Talks

• [ 65238 ] Peer Prediction for Learning Agents
• [ 65239 ] MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
• [ 65240 ] Structural Pruning via Latency-Saliency Knapsack
• [ 65241 ] On the Strong Correlation Between Model Invariance and Generalization
• [ 65243 ] Zero-Sum Stochastic Stackelberg Games
• [ 65245 ] Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?

Q&A on RocketChat immediately following Lightning Talks

• [ 65213 ] How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
• [ 65214 ] HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process
• [ 65215 ] LGDN: Language-Guided Denoising Network for Video-Language Modeling
• [ 65217 ] When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
• [ 65218 ] Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
• [ 65219 ] Neural-Symbolic Entangled Framework for Complex Query Answering
• [ 65220 ] DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
• [ 65221 ] A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
• [ 65222 ] Differentially Private Learning with Margin Guarantees
• [ 65223 ] DDXPlus: A New Dataset For Automatic Medical Diagnosis

Q&A on RocketChat immediately following Lightning Talks

• [ 65268 ] EcoFormer: Energy-Saving Attention with Linear Complexity
• [ 65269 ] Fast Vision Transformers with HiLo Attention
• [ 65270 ] VTC-LFC: Vision Transformer Compression with Low-Frequency Components
• [ 65271 ] SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
• [ 65272 ] Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
• [ 65274 ] RecursiveMix: Mixed Learning with History
• [ 65275 ] Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
• [ 65276 ] Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
• [ 65277 ] MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
• [ 65278 ] Feature-Proxy Transformer for Few-Shot Segmentation

Q&A on RocketChat immediately following Lightning Talks

• [ 65246 ] GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions
• [ 65247 ] PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories
• [ 65248 ] AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
• [ 65249 ] Efficient and Effective Augmentation Strategy for Adversarial Training
• [ 65250 ] 4D Unsupervised Object Discovery
• [ 65251 ] SNAKE: Shape-aware Neural 3D Keypoint Field
• [ 65252 ] Few-Shot Non-Parametric Learning with Deep Latent Variable Model
• [ 65253 ] Neural Shape Deformation Priors
• [ 65254 ] Few-Shot Continual Active Learning by a Robot
• [ 65255 ] Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
• [ 65256 ] Segmenting Moving Objects via an Object-Centric Layered Representation

Q&A on RocketChat immediately following Lightning Talks

• [ 65279 ] Quantized Training of Gradient Boosting Decision Trees
• [ 65280 ] Coded Residual Transform for Generalizable Deep Metric Learning
• [ 65282 ] Pyramid Attention For Source Code Summarization
• [ 65283 ] MorphTE: Injecting Morphology in Tensorized Embeddings
• [ 65284 ] Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
• [ 65285 ] Out-of-Distribution Detection via Conditional Kernel Independence Model
• [ 65286 ] Weak-shot Semantic Segmentation via Dual Similarity Transfer
• [ 65287 ] MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
• [ 65288 ] Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
• [ 65289 ] ViSioNS: Visual Search in Natural Scenes Benchmark

Q&A on RocketChat immediately following Lightning Talks

• [ 65257 ] Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
• [ 65258 ] MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification
• [ 65259 ] Two-Stream Network for Sign Language Recognition and Translation
• [ 65260 ] Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
• [ 65261 ] Egocentric Video-Language Pretraining
• [ 65262 ] Multi-dataset Training of Transformers for Robust Action Recognition
• [ 65264 ] Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
• [ 65265 ] An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
• [ 65266 ] MultiScan: Scalable RGBD scanning for 3D environments with articulated objects
• [ 65267 ] A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

Q&A on RocketChat immediately following Lightning Talks