yusuke1997/dblp-id-title huggingface dataset:
Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation
Latent Bottlenecked Attentive Neural Processes
MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
Selfless Sequential Learning
HiLLoC: lossless image compression with hierarchical latent variable models
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Hypergraph Dynamic System
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
Perceptual Group Tokenizer: Building Perception with Iterative Grouping
When Does Second-Order Optimization Speed Up Training?
Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
Dynamics-Informed Protein Design with Structure Conditioning
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
Challenges in Disentangling Independent Factors of Variation
Meta-Learning for Batch Mode Active Learning
CREAM: Consistency Regularized Self-Rewarding Language Models
Dimension Agnostic Neural Processes
LLM-based Typed Hyperresolution for Commonsense Reasoning with Knowledge Bases
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
SketchODE: Learning neural sketch representation in continuous time
Fast-ELECTRA for Efficient Pre-training
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Implicit Neural Surface Deformation with Explicit Velocity Fields
Conditional Generative Modeling via Learning the Latent Space
Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
GAIA: a benchmark for General AI Assistants
The Information Geometry of Unsupervised Reinforcement Learning
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
Domain Adaptive Multibranch Networks
Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels
HOPE for a Robust Parameterization of Long-memory State Space Models
Mask2tasks: Leveraging Segmentation to Enhance Classification Performance in histopathological colorectal Images
Learning Geometric Reasoning Networks For Robot Task And Motion Planning
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Improved Regret Bounds for Non-Convex Online-Within-Online Meta Learning
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
VersVideo: Leveraging Enhanced Temporal Diffusion Models for Versatile Video Generation
MultiModalQA: complex question answering over text, tables and images
Reliability of CKA as a Similarity Measure in Deep Learning
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
GradSign: Model Performance Inference with Theoretical Insights
ProtoSnap: Prototype Alignment For Cuneiform Signs
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
Win-Win: Training High-Resolution Vision Transformers from Two Windows
RingAttention with Blockwise Transformers for Near-Infinite Context
Single Episode Policy Transfer in Reinforcement Learning
Solving the Rubik's Cube with Approximate Policy Iteration
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
Calibrating Sequence likelihood Improves Conditional Language Generation
Provence: efficient and robust context pruning for retrieval-augmented generation
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Tailoring Language Generation Models under Total Variation Distance
Gradient Gating for Deep Multi-Rate Learning on Graphs
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation
SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency
GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Eliminating Position Bias of Language Models: A Mechanistic Approach
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
A Unified and General Framework for Continual Learning
A Semantic Invariant Robust Watermark for Large Language Models
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Resilient Backpropagation (Rprop) for Batch-learning in TensorFlow
Fast and Accurate Blind Flexible Docking
Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time
Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors
An Evolved Universal Transformer Memory
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Consistent Flow Distillation for Text-to-3D Generation
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA
DFPC: Data flow driven pruning of coupled channels without data
Forewarned is Forearmed: Harnessing LLMs for Data Synthesis via Failure-induced Exploration
Manifold Constraint Reduces Exposure Bias in Accelerated Diffusion Sampling
Programming Refusal with Conditional Activation Steering
Shallow diffusion networks provably learn hidden low-dimensional structure
Attention as a Hypernetwork
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation
TULIP: Token-length Upgraded CLIP
STAR: Synthesis of Tailored Architectures
FIG: Flow with Interpolant Guidance for Linear Inverse Problems
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
Convolutional Deep Kernel Machines
Transformer Meets Twicing: Harnessing Unattended Residual Information
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Batch normalization is sufficient for universal function approximation in CNNs
Generalizable Motion Planning via Operator Learning
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Learning Multimodal Data Augmentation in Feature Space
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning
Ranking-aware adapter for text-driven image ordering with CLIP
Spatio-Temporal Approximation: A Training-Free SNN Conversion for Transformers
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
One-shot Empirical Privacy Estimation for Federated Learning
ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms
Graph Metanetworks for Processing Diverse Neural Architectures
Identifying Policy Gradient Subspaces
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs
Safe Collaborative Filtering
Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
EigenGame Unloaded: When playing games is better than optimizing
Training Robust Ensembles Requires Rethinking Lipschitz Continuity
Robust-PIFu: Robust Pixel-aligned Implicit Function for 3D Human Digitalization from a Single Image
KinFormer: Generalizable Dynamical Symbolic Regression for Catalytic Organic Reaction Kinetics
Out-of-Variable Generalisation for Discriminative Models
Building Deep Equivariant Capsule Networks
Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation
Probabilistic Geometric Principal Component Analysis with application to neural data
ViSAGe: Video-to-Spatial Audio Generation
On the Expressive Power of Overlapping Architectures of Deep Learning
Topology-aware Robust Optimization for Out-of-Distribution Generalization
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models
Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure
Adversarial Mixup Resynthesizers
Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets
Fisher-Legendre (FishLeg) optimization of deep neural networks
Dataset Ownership Verification in Contrastive Pre-trained Models
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
BoneMet: An Open Large-Scale Multi-Modal Murine Dataset for Breast Cancer Bone Metastasis Diagnosis and Prognosis
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
Contrastive Meta-Learning for Partially Observable Few-Shot Learning
Reframing Structure-Based Drug Design Model Evaluation via Metrics Correlated to Practical Needs
EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
DenseGrounding: Improving Dense Language-Vision Semantics for Ego-centric 3D Visual Grounding
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
On Self-Supervised Image Representations for GAN Evaluation
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs
Improving Equivariant Networks with Probabilistic Symmetry Breaking
Herald: A Natural Language Annotated Lean 4 Dataset
PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
Agents' Room: Narrative Generation through Multi-step Collaboration
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions
Unlocking the Potential of Model Calibration in Federated Learning
Lasso Bandit with Compatibility Condition on Optimal Arm
Multiple-Attribute Text Rewriting
Practical Hyperparameter Optimization for Deep Learning
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation
Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
Efficient Representation of Low-Dimensional Manifolds using Deep Networks
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
Learning Wasserstein Embeddings
On the Convergence of FedAvg on Non-IID Data
Training-free Camera Control for Video Generation
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks
Your Weak LLM is Secretly a Strong Teacher for Alignment
Discrete Representations Strengthen Vision Transformer Robustness
On the generalization capacity of neural networks during generic multimodal reasoning
Compositional Law Parsing with Latent Random Functions
Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning
Sample and Computation Redistribution for Efficient Face Detection
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
How to train your MAML
The Belief State Transformer
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
Decomposition Polyhedra of Piecewise Linear Functions
Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation
Stochastic No-regret Learning for General Games with Variance Reduction
Memory Efficient Transformer Adapter for Dense Predictions
Robust Model-Based Optimization for Challenging Fitness Landscapes
To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning
Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
Scalable Decentralized Learning with Teleportation
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
The Uncanny Similarity of Recurrence and Depth
A Theoretical Framework for Partially-Observed Reward States in RLHF
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph
High-quality Text-to-3D Character Generation with SparseCubes and Sparse Transformers
Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports
Synthetic and Natural Noise Both Break Neural Machine Translation
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning
Token Merging: Your ViT But Faster
Generative Multi-Adversarial Networks
Towards a Unified and Verified Understanding of Group-Operation Networks
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
Distributionally Robust Recourse Action
D2 Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Model-based Causal Bayesian Optimization
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS
Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount
Automatic Curriculum Expert Iteration for Reliable LLM Reasoning
Learning Gain Map for Inverse Tone Mapping
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis
Intriguing Properties of Data Attribution on Diffusion Models
Should I Run Offline Reinforcement Learning or Behavioral Cloning?
How Low Can We Go: Trading Memory for Error in Low-Precision Training
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Semantic Interpolation in Implicit Models
PABBO: Preferential Amortized Black-Box Optimization
MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow
Fixup Initialization: Residual Learning Without Normalization
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward
OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer
PAE: Reinforcement Learning from External Knowledge for Efficient Exploration
Self-labelling via simultaneous clustering and representation learning
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Implementation Matters in Deep RL: A Case Study on PPO and TRPO
Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Conference Track Proceedings
Understanding Deep Neural Networks with Rectified Linear Units
Meta Dropout: Learning to Perturb Latent Features for Generalization
Label super-resolution networks
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Contextualized Scene Imagination for Generative Commonsense Reasoning
Spurious Forgetting in Continual Learning of Language Models
Provable Compositional Generalization for Object-Centric Learning
NAS evaluation is frustratingly hard
Learning Vortex Dynamics for Fluid Inference and Prediction
SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP
Language Model Inversion
Text4Seg: Reimagining Image Segmentation as Text Generation
Approximate Nearest Neighbor Search through Modern Error-Correcting Codes
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography
Deep Complex Networks
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
GLOMA: Global Video Text Spotting with Morphological Association
3D Segmenter: 3D Transformer based Semantic Segmentation via 2D Panoramic Distillation
Revealing interpretable object representations from human behavior
Memory Architectures in Recurrent Neural Network Language Models
Causal Identification for Complex Functional Longitudinal Studies
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations
CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design
Sketch2Diagram: Generating Vector Diagrams from Hand-Drawn Sketches
Simplicial Hopfield networks
Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks
Bridging Compressed Image Latents and Multimodal Large Language Models
A Comparison of Hamming Errors of Representative Variable Selection Methods
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training
In-Context Learning Dynamics with Random Binary Sequences
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
EquiMod: An Equivariance Module to Improve Visual Instance Discrimination
CoLiDE: Concomitant Linear DAG Estimation
Divide and Conquer Networks
Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Robust Local Features for Improving the Generalization of Adversarial Training
Calibrating LLMs with Information-Theoretic Evidential Deep Learning
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Why Does the Effective Context Length of LLMs Fall Short?
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Budgeted Online Continual Learning by Adaptive Layer Freezing and Frequency-based Sampling
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer
Neural Outlier Rejection for Self-Supervised Keypoint Learning
Policy Transfer with Strategy Optimization
Boosting Robustness Certification of Neural Networks
Flow: Modularized Agentic Workflow Automation
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
Fundamental Limits in Formal Verification of Message-Passing Neural Networks
One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention
FreDF: Learning to Forecast in the Frequency Domain
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation
ThermalGaussian: Thermal 3D Gaussian Splatting
NfgTransformer: Equivariant Representation Learning for Normal-form Games
Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels
UV-Attack: Physical-World Adversarial Attacks on Person Detection via Dynamic-NeRF-based UV Mapping
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
Zero and Few-shot Semantic Parsing with Ambiguous Inputs
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
A Zest of LIME: Towards Architecture-Independent Model Distances
State Representation Learning Using an Unbalanced Atlas
A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
Kernelised Normalising Flows
Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning
RAIN: Your Language Models Can Align Themselves without Finetuning
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Progressive Distillation for Fast Sampling of Diffusion Models
BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation
Dropout with Expectation-linear Regularization
Prompt Gradient Projection for Continual Learning
Programmatic Reinforcement Learning without Oracles
UniDrive: Towards Universal Driving Perception Across Camera Configurations