yusuke1997/dblp-id-title huggingface dataset:
A Stochastic Approach to the Subset Selection Problem via Mirror Descent
Optimizing MPJPE promotes miscalibration in multi-hypothesis human pose lifting
PseDet: Revisiting the Power of Pseudo Label in Incremental Object Detection
Minimum Variance Unbiased N: M Sparsity for the Neural Gradients
On the application and impact of ε-DP and fairness in ambulance engagement time prediction
Multi-modal brain encoding models for multi-modal stimuli
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
What are the Statistical Limits of Offline RL with Linear Function Approximation?
A Shared Encoder for Multi-Source Hyperspectral Images
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
Activation-level uncertainty in deep neural networks
Composing Complex Skills by Learning Transition Policies
Differentially Private Meta-Learning
Tent: Fully Test-Time Adaptation by Entropy Minimization
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
Chain of Log-Concave Markov Chains
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
Critical Percolation as a Framework to Analyze the Training of Deep Networks
Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains
Benchmarking Constraint Inference in Inverse Reinforcement Learning
Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Learning continuous-time PDEs from sparse data with graph neural networks
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks
CPT: Efficient Deep Neural Network Training via Cyclic Precision
Mass-Editing Memory in a Transformer
CT-Net: Channel Tensorization Network for Video Classification
Model-based reinforcement learning for biological sequence design
Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching
Aspect-based Question Generation
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Using spiking neural networks to assist fine art and philology study: to classify styles of Chinese calligraphy with minimal computing power
Counterfactual Concept Bottleneck Models
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps
GEASS: Neural causal feature selection for high-dimensional biological data
Towards Robustness Certification Against Universal Perturbations
Modular Lifelong Reinforcement Learning via Neural Composition
Bayesian Analysis of Combinatorial Gaussian Process Bandits
Memory Gym: Partially Observable Challenges to Memory-Based Agents
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
Evolving Populations of Diverse RL Agents with MAP-Elites
Transformers Provably Solve Parity Efficiently with Chain of Thought
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks
Language-Interfaced Tabular Oversampling via Progressive Imputation and Self-Authentication
Residual Stream Analysis with Multi-Layer SAEs
SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation
Discovering Motor Programs by Recomposing Demonstrations
Learning and Analyzing Vector Encoding of Symbolic Representation
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
LEAF: A Learnable Frontend for Audio Classification
Understanding Certified Training with Interval Bound Propagation
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale
Expressivity of ReLU-Networks under Convex Relaxations
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
Fooling SHAP with Stealthily Biased Sampling
Massively Multilingual Sparse Word Representations
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation
Swing-by Dynamics in Concept Learning and Compositional Generalization
MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
Causal Graphical Models for Vision-Language Compositional Understanding
Learning Neural Generative Dynamics for Molecular Conformation Generation
Towards General Function Approximation in Zero-Sum Markov Games
TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram
Hidden Markov Transformer for Simultaneous Machine Translation
Learning Awareness Models
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Learning to Understand Goal Specifications by Modelling Reward
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
MANTRA: The Manifold Triangulations Assemblage
Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances
In defense of parameter sharing for model-compression
Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Towards a learning theory of representation alignment
Locality Sensitive Avatars From Video
Improving Unsupervised Constituency Parsing via Maximizing Semantic Information
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
Duration-of-Stay Storage Assignment under Uncertainty
The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
Learning to Generate Columns with Application to Vertex Coloring
Attention-based Interpretability with Concept Transformers
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
The Dark Side of AutoML: Towards Architectural Backdoor Search
AutoChunk: Automated Activation Chunk for Memory-Efficient Deep Learning Inference
Beyond Time: Accurately Estimating the Fair Value of Stocks with Machine Learning and Fundamental Data
Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Image as Set of Points
COAT: Compressing Optimizer states and Activations for Memory-Efficient FP8 Training
Lawma: The Power of Specialization for Legal Annotation
From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics
Structure Language Models for Protein Conformation Generation
Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
Combining Label Propagation and Simple Models out-performs Graph Neural Networks
DOM-Q-NET: Grounded RL on Structured Language
SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Capsules with Inverted Dot-Product Attention Routing
CyberHost: A One-stage Diffusion Framework for Audio-driven Talking Body Generation
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
Multiscale Score Matching for Out-of-Distribution Detection
Accurate Learning of Graph Representations with Graph Multiset Pooling
Minimax Optimization with Smooth Algorithmic Adversaries
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
DECO: Unleashing the Potential of ConvNets for Query-based Detection and Segmentation
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
Versatile Neural Processes for Learning Implicit Neural Representations
Chain-of-Experts: When LLMs Meet Complex Operations Research Problems
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
Towards convergence to Nash equilibria in two-team zero-sum games
Evaluating Distributional Distortion in Neural Language Modeling
Does SGD really happen in tiny subspaces?
Single-shot General Hyper-parameter Optimization for Federated Learning
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Watch Less, Do More: Implicit Skill Discovery for Video-Conditioned Policy
Few-shot Hybrid Domain Adaptation of Image Generator
FlickerFusion: Intra-trajectory Domain Generalizing Multi-agent Reinforcement Learning
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
CPPO: Continual Learning for Reinforcement Learning with Human Feedback
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Permutation Equivariant Models for Compositional Generalization in Language
Multiplicative LSTM for sequence modelling
Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory
Data-centric Prediction Explanation via Kernelized Stein Discrepancy
DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
Multi-view Recurrent Neural Acoustic Word Embeddings
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Bootstrapped Meta-Learning
MMSearch: Unveiling the Potential of Large Models as Multi-modal Search Engines
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
Execution-Guided Neural Program Synthesis
What does it take to generate natural textures?
A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
On the Word Boundaries of Emergent Languages Based on Harris's Articulation Scheme
Controlling Directions Orthogonal to a Classifier
Enforcing robust control guarantees within neural network policies
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
Compositional languages emerge in a neural iterated learning model
Optimizing importance weighting in the presence of sub-population shifts
Cameras as Rays: Pose Estimation via Ray Diffusion
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
SYNC: Safety-Aware Neural Control for Stabilizing Stochastic Delay-Differential Equations
Masked Distillation Advances Self-Supervised Transformer Architecture Search
Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations
Contextual Bandits with Online Neural Regression
Neural Auto-designer for Enhanced Quantum Kernels
PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection
On the Importance and Applicability of Pre-Training for Federated Learning
Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data
Learning Spatiotemporal Dynamical Systems from Point Process Observations
WiFi CSI-based Long-Range Person Localization Using Directional Antennas
Learning Invariances for Policy Generalization
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Sparsity-Constrained Optimal Transport
Wavelet-based Positional Representation for Long Context
Understanding the role of importance weighting for deep learning
Vector-quantized Image Modeling with Improved VQGAN
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning
Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features
Convolutional Conditional Neural Processes
On Incorporating Inductive Biases into VAEs
Semantic embeddings for program behaviour patterns
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
SimulPL: Aligning Human Preferences in Simultaneous Machine Translation
PooDLe🐩: Pooled and dense self-supervised learning from naturalistic videos
ImagenHub: Standardizing the evaluation of conditional image generation models
Piecewise linear activations substantially shape the loss surfaces of neural networks
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
Graph Partition Neural Networks for Semi-Supervised Classification
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
Interactive Adjustment for Human Trajectory Prediction with Individual Feedback
Neural Network Branching for Neural Network Verification
NIRo: A Metric to capture non-iid robustness for Federated Learning Algorithms
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
Towards Robust Multi-Modal Reasoning via Model Selection
Benign Overfitting in Out-of-Distribution Generalization of Linear Models
FairGBM: Gradient Boosting with Fairness Constraints
Capsule Graph Neural Network
To Tackle Adversarial Transferability: A Novel Ensemble Training Method with Fourier Transformation
Understanding Long Videos with Multimodal Language Models
End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler
FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution
Learning with Auxiliary Activation for Memory-Efficient Training
A rotation-equivariant convolutional neural network model of primary visual cortex
Partial Gromov-Wasserstein Metric
Pose Modulated Avatars from Video
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures
Can LLM-Generated Misinformation Be Detected?
DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
Truthful Self-Play
Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation
What Do We Mean by Generalization in Federated Learning?
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark for Large Language Models
Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation
DataMan: Data Manager for Pre-training Large Language Models
UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks
The Effectiveness of a two-Layer Neural Network for Recommendations
On Detecting Adversarial Perturbations
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee
Learning Simultaneous Navigation and Construction in Grid Worlds
DICE: Data Influence Cascade in Decentralized Learning
Learning to solve the credit assignment problem
Counterfactual Density Estimation using Kernel Stein Discrepancies
wav2tok: Deep Sequence Tokenizer for Audio Retrieval
Recurrent Environment Simulators
Think while You Generate: Discrete Diffusion with Planned Denoising
SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction
Statistical Efficiency of Score Matching: The View from Isoperimetry
Learning with Language-Guided State Abstractions
Robust and Generalizable Visual Representation Learning via Random Convolutions
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
Deep Generative Symbolic Regression
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory
Improving Non-Autoregressive Translation Models Without Distillation
RotoGrad: Gradient Homogenization in Multitask Learning
Competitive Fair Scheduling with Predictions
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions
Towards Continual Knowledge Learning of Language Models
Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
Deconstructing the Regularization of BatchNorm
Near-Optimal Quantum Algorithm for Minimizing the Maximal Loss
Fooling a Complete Neural Network Verifier
Analogy-Forming Transformers for Few-Shot 3D Parsing
GFlowNets with Human Feedback
Language modeling via stochastic processes
Categorical Reparameterization with Gumbel-Softmax
Multi-View Representation is What You Need for Point-Cloud Pre-Training
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Revisiting Populations in multi-agent Communication
XAIguiFormer: explainable artificial intelligence guided transformer for brain disorder identification
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
Preference Transformer: Modeling Human Preferences using Transformers for RL
Expert-based reward function training: the novel method to train sequence generators
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel
KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement
Efficient Self-supervised Vision Transformers for Representation Learning
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
Using the Polyak Step Size in training Convolutional Neural Networks
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
Towards Out-of-Modal Generalization without Instance-level Modal Correspondence
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Self-Adversarial Learning with Comparative Discrimination for Text Generation
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
FaiREE: fair classification with finite-sample and distribution-free guarantee
Decoding Natural Images from EEG for Object Recognition
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
On the "steerability" of generative adversarial networks
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Lipschitz Recurrent Neural Networks
Select to Perfect: Imitating desired behavior from large multi-agent data
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
Streaming Algorithms For ℓp Flows and ℓp Regression
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation
An Online Learning Approach to Generative Adversarial Networks
Weakly Supervised Video Scene Graph Generation via Natural Language Supervision
Contrastive Learning from Synthetic Audio Doppelgängers
Learning Invariant Representations Of Planar Curves
Explainable GNN-Based Models over Knowledge Graphs
Pareto Policy Pool for Model-based Offline Reinforcement Learning
Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks
Expressive Losses for Verified Robustness via Convex Combinations
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
Divergence-aware Federated Self-Supervised Learning
Energy-based models for atomic-resolution protein conformations
CLAP: Collaborative Adaptation for Patchwork Learning
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Episodic Reinforcement Learning with Associative Memory
Efficient Backpropagation with Variance Controlled Adaptive Sampling
Continuous-time identification of dynamic state-space models by deep subspace encoding
Visual Recognition with Deep Nearest Centroids
Physics-informed Temporal Difference Metric Learning for Robot Motion Planning
Regularizing and Optimizing LSTM Language Models
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Domain Adversarial Training: A Game Perspective
Towards Understanding the Data Dependency of Mixup-style Training
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation
Joint Training of Ratings and Reviews with Recurrent Recommender Networks
Utilitarian Algorithm Configuration for Infinite Parameter Spaces
Identifiability for Gaussian Processes with Holomorphic Kernels
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice
PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows
Depth Separation with Multilayer Mean-Field Networks
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Collapse of Self-trained Language Models