yusuke1997/dblp-id-title huggingface dataset:
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
Neural Wave Equation for Irregularly Sampled Sequence Data
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving
Learning Hierarchical Polynomials of Multiple Nonlinear Features
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Loss is its own Reward: Self-Supervision for Reinforcement Learning
SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling
Interpretable Complex-Valued Neural Networks for Privacy Protection
Learning from negative feedback, or positive feedback or both
Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate
FunkNN: Neural Interpolation for Functional Generation
Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric
Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Teaching Human Behavior Improves Content Understanding Abilities Of VLMs
HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction
Fraternal Dropout
N-ForGOT: Towards Not-forgetting and Generalization of Open Temporal Graph Learning
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
In Search for a SAT-friendly Binarized Neural Network Architecture
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Understanding Expressivity of GNN in Rule Learning
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring
On Difficulties of Attention Factorization through Shared Memory
Few-shot Text Classification with Distributional Signatures
LiftedCL: Lifting Contrastive Learning for Human-Centric Perception
Predicting Inductive Biases of Pre-Trained Models
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning
Causal Discovery Using Proxy Variables
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
WeatherGFM: Learning a Weather Generalist Foundation Model via In-context Learning
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging
Exploring a Principled Framework for Deep Subspace Clustering
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Generative Modeling with Optimal Transport Maps
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Teaching Language Models to Hallucinate Less with Synthetic Tasks
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
MGAN: Training Generative Adversarial Nets with Multiple Generators
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Towards Parametric Robust Activation Functions in Adversarial Machine Learning
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads
Leave-One-Out Stable Conformal Prediction
Privileged Sensing Scaffolds Reinforcement Learning
Unified Human-Scene Interaction via Prompted Chain-of-Contacts
Learning to cluster in order to transfer across domains and tasks
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
Stabilized Neural Prediction of Potential Outcomes in Continuous Time
Improved Convergence of Differential Private SGD with Gradient Clipping
Mitigate the Gap: Improving Cross-Modal Alignment in CLIP
SP2 : A Second Order Stochastic Polyak Method
Training Data Eigenvector Dynamics in the EigenPro Implementation of the Neural Tangent Kernel and Recursive Feature Machines
Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning
STAFF: Speculative Coreset Selection for Task-Specific Fine-tuning
Protecting against simultaneous data poisoning attacks
Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility
Subgradient Descent Learns Orthogonal Dictionaries
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
Efficient Subgraph GNNs by Learning Effective Selection Policies
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Chaotic Transformers for Deep Reinforcement Learning in Algorithmic Trading
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
Neu0
The Curse of Diversity in Ensemble-Based Exploration
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning
Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation
On Redundancy and Diversity in Cell-based Neural Architecture Search
A Primal-Dual Framework for Transformers and Neural Networks
Sliced Wasserstein Auto-Encoders
Distributionally Robust Neural Networks
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
A Computational Framework for Modeling Emergence of Color Vision in the Human Brain
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Adaptive Gradient Clipping for Robust Federated Learning
M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model
Auto-Encoding Sequential Monte Carlo
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance
Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities
Disentangling neural mechanisms for perceptual grouping
Sliced Wasserstein Estimation with Control Variates
Effects of Single-Attribute Control on the Music Generated by FIGARO
Role of Over-Parameterization in Generalization of 3-layer ReLU Networks
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solver
OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation
DistillSpec: Improving Speculative Decoding via Knowledge Distillation
PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
Large (Vision) Language Models are Unsupervised In-Context Learners
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
ALAM: Averaged Low-Precision Activation for Memory-Efficient Training of Transformer Models
Exploring Loss Design Techniques For Decision Tree Robustness To Label Noise
Fluctuation-dissipation relations for stochastic gradient descent
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
AdaGrad under Anisotropic Smoothness
On the Transfer of Disentangled Representations in Realistic Settings
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Generative predecessor models for sample-efficient imitation learning
State Alignment-based Imitation Learning
Geometry of Lightning Self-Attention: Identifiability and Dimension
Sparse Distributed Memory is a Continual Learner
Neural Pruning via Growing Regularization
Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection
A Benchmark for Semantic Sensitive Information in LLMs Outputs
Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Learning to Complete Code with Sketches
Targeted Attack Improves Protection against Unauthorized Diffusion Customization
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
Denoising with a Joint-Embedding Predictive Architecture
On the Universality of Rotation Equivariant Point Cloud Networks
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
ReMasker: Imputing Tabular Data with Masked Autoencoding
Chunk-Distilled Language Modeling
Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
Bayesian Neural Network Priors Revisited
VQ-TR: Vector Quantized Attention for Time Series Forecasting
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
A new dog learns old tricks: RL finds classic optimization algorithms
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
Learning Value Functions in Deep Policy Gradients using Residual Variance
Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
3D Reconstruction with Generalizable Neural Fields using Scene Priors
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
Emu: Generative Pretraining in Multimodality
Regulatory DNA Sequence Design with Reinforcement Learning
NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields
Lr0.Fm: low-Resolution Zero-Shot Classification Benchmark for Foundation Models
Turning large language models into cognitive models
Minimum width for universal approximation using ReLU networks on compact domain
SaProt: Protein Language Modeling with Structure-aware Vocabulary
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Neural TTS Stylization with Adversarial and Collaborative Games
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective
Breaking Neural Network Scaling Laws with Modularity
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction
Jointly Learning Visual and Auditory Speech Representations from Raw Data
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Adaptive Distance Message Passing From the Multi-Relational Edge View
LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions
Decomposed Prompting: A Modular Approach for Solving Complex Tasks
Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models
Discovering Influential Neuron Path in Vision Transformers
Self-Supervised Image Denoising with Swin Transformer
Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
TabM: Advancing tabular deep learning with parameter-efficient ensembling
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
On The Relative Error of Random Fourier Features for Preserving Kernel Distance
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
GAIN: On the Generalization of Instructional Action Understanding
Copula Conformal prediction for multi-step time series prediction
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
Recursive Time Series Data Augmentation
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
DEGREE: Decomposition Based Explanation for Graph Neural Networks
Learning from Positive and Unlabeled Data with a Selection Bias
FasterViT: Fast Vision Transformers with Hierarchical Attention
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
Modulate Your Spectrum in Self-Supervised Learning
Contrastive Representation Distillation
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
Multi-Agent MDP Homomorphic Networks
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Estimating Shape Distances on Neural Representations with Limited Samples
Efficient Reinforcement Learning with Large Language Model Priors
Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape View
HyperNetworks
Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model
AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies
Hymba: A Hybrid-head Architecture for Small Language Models
Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation
MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Transformer-Patcher: One Mistake Worth One Neuron
FreqPrior: Improving Video Diffusion Models with Frequency Filtering Gaussian Noise
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers
Primal Wasserstein Imitation Learning
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability
Exploring the Limits of Large Scale Pre-training
Comparing noisy neural population dynamics using optimal transport distances
Rethinking Embedding Coupling in Pre-trained Language Models
Emergent Translation in Multi-Agent Communication
Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Fast Geometric Projections for Local Robustness Certification
Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
Constrained Physical-Statistics Models for Dynamical System Identification and Prediction
Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late In Training
R-MAE: Regions Meet Masked Autoencoders
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
Refining CLIP's Spatial Awareness: A Visual-Centric Perspective
A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations
Multi-Agent Dual Learning
HyperAttention: Long-context Attention in Near-Linear Time
Filter-Recovery Network for Multi-Speaker Audio-Visual Speech Separation
Think before you speak: Training Language Models With Pause Tokens
GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
Contextual Convolutional Networks
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
Towards Principled Representation Learning from Videos for Reinforcement Learning
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs
A Distributional Approach to Controlled Text Generation
Zero Bubble (Almost) Pipeline Parallelism
PixelNN: Example-based Image Synthesis
Towards Understanding the Universality of Transformers for Next-Token Prediction
Accelerating neural network training: An analysis of the AlgoPerf competition
Consequentialist conditional cooperation in social dilemmas with imperfect information
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Explore Theory of Mind: program-guided adversarial data generation for theory of mind reasoning
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
ReSi: A Comprehensive Benchmark for Representational Similarity Measures
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Spike-inspired rank coding for fast and accurate recurrent neural networks
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models
Fusing Vision and Language Models to Generate Sequence of Recipe Images from Steps
Gradient Origin Networks
Training Free Exponential Context Extension via Cascading KV Cache
Tailoring Mixup to Data for Calibration
Neural Functional Programming
Revisiting Link Prediction: a data perspective
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation
Improving Offline RL by Blending Heuristics
Automated Design using Neural Networks and Gradient Descent
Dynamic Discounted Counterfactual Regret Minimization
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Batch-shaping for learning conditional channel gated networks
Language Guided Skill Discovery
Improving Semantic Understanding in Speech Language Models via Brain-tuning
Controllable Context Sensitivity and the Knob Behind It
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
The Second Tiny Papers Track at ICLR 2024, Tiny Papers @ ICLR 2024, Vienna, Austria, May 11, 2024
GreaseLM: Graph REASoning Enhanced Language Models
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Dual Associated Encoder for Face Restoration
Pay Attention to Features, Transfer Learn Faster CNNs
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
Mitigating Spurious Correlations in Zero-Shot Multimodal Models
Progressive Prompts: Continual Learning for Language Models
Free Lunch for Few-shot Learning: Distribution Calibration
GAMR: A Guided Attention Model for (visual) Reasoning
Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Object Dynamics Distillation for Scene Decomposition and Representation
Learning to Grow Pretrained Models for Efficient Transformer Training
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
TD-Paint: Faster Diffusion Inpainting Through Time-Aware Pixel Conditioning
Linear Connectivity Reveals Generalization Strategies
Imitation Learning via Off-Policy Distribution Matching
MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods
Isometric Regularization for Manifolds of Functional Data
Diffusion2: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models
How I Learned to Stop Worrying and Love Retraining
Federated Learning Based on Dynamic Regularization
Robust System Identification: Finite-sample Guarantees and Connection to Regularization
DiffEnc: Variational Diffusion with a Learned Encoder
Memorization Capacity of Multi-Head Attention in Transformers
A CMDP-within-online framework for Meta-Safe Reinforcement Learning
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities
Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation
Backtracking Mathematical Reasoning of Language Models to the Pretraining Data
HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
Learning Two-layer Neural Networks with Symmetric Inputs
Zero-Shot Classification Reveals Potential Positive Sentiment Bias in African Languages Translations
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
Divergence-Regularized Discounted Aggregation: Equilibrium Finding in Multiplayer Partially Observable Stochastic Games
Meta-learning with negative learning rates
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
PromptTTS 2: Describing and Generating Voices with Text Prompt
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Multi-granularity Correspondence Learning from Long-term Noisy Videos
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Generating Molecules via Chemical Reactions
Global Relational Models of Source Code
Unveiling and Manipulating Prompt Influence in Large Language Models
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Pareto Prompt Optimization
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Sentence-level Prompts Benefit Composed Image Retrieval
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
Encoding Recurrence into Transformers
Dynamic Human AI Collaboration
Distributed Differential Privacy in Multi-Armed Bandits
Restricted Boltzmann Machines provide an accurate metric for retinal responses to visual stimuli
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue