yusuke1997/dblp-id-title huggingface dataset:
Nonlinear Sequence Embedding by Monotone Variational Inequality
Activation Maximization Generative Adversarial Nets
Retrieval-based Controllable Molecule Generation
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
Generative Models as a Data Source for Multiview Representation Learning
Transferring Labels to Solve Annotation Mismatches Across Object Detection Datasets
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Adaptive Universal Generalized PageRank Graph Neural Network
Random Feature Attention
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Task-Induced Representation Learning
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
Rethinking Light Decoder-based Solvers for Vehicle Routing Problems
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Computation Reallocation for Object Detection
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Can LLMs Learn a New Language on the Fly? A Case Study on Zhuang
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
Difference-of-submodular Bregman Divergence
Meta-Q-Learning
Why (and When) does Local SGD Generalize Better than SGD?
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Improving Deep Regression with Tightness
Creating Training Sets via Weak Indirect Supervision
Kernel Neural Optimal Transport
Balanced Ranking with Relative Centrality: A multi-core periphery perspective
LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
On Stochastic Contextual Bandits with Knapsacks in Small Budget Regime
Lifelong Learning of Compositional Structures
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness
GReTo: Remedying dynamic graph topology-task discordance via target homophily
PixelVAE: A Latent Variable Model for Natural Images
Certified Defenses against Adversarial Examples
Phase Transitions for the Information Bottleneck in Representation Learning
Adversarial Diversity in Hanabi
Communication Algorithms via Deep Learning
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models
Adversarial Feature Learning
Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Label-Noise Robust Diffusion Models
Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer
Synergistic Patch Pruning for Vision Transformer: Unifying Intra- & Inter-Layer Patch Importance
Reconstruction-Guided Policy: Enhancing Decision-Making through Agent-Wise State Consistency
Conformal Inductive Graph Neural Networks
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Augmenting Collective Intelligence through Belbin's Team Roles
Variance Reduction With Sparse Gradients
VDT: General-purpose Video Diffusion Transformers via Mask Modeling
Is Large-scale Pretraining the Secret to Good Domain Generalization?
iGraphMix: Input Graph Mixup Method for Node Classification
Effective Data Augmentation With Diffusion Models
Weakly Supervised Clustering by Exploiting Unique Class Count
Defending against Adversarial Audio via Diffusion Model
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Video Scene Graph Generation from Single-Frame Weak Supervision
Learning transport cost from subset correspondence
Inspection and Control of Self-Generated-Text Recognition Ability in Llama3-8b-Instruct
Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation
Dilated convolution with learnable spacings
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
On the insufficiency of existing momentum schemes for Stochastic Optimization
Provably Auditing Ordinary Least Squares in Low Dimensions
DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory
Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling
Faithful Vision-Language Interpretation via Concept Bottleneck Models
Distribution-Free Data Uncertainty for Neural Network Regression
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains
Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
Quantifying Generalization Complexity for Large Language Models
Code Representation Learning at Scale
Graph Attention Networks
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors
Representing Partial Programs with Blended Abstract Semantics
Recurrent neural circuits for contour detection
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Training a Subsampling Mechanism in Expectation
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
GOAL: A Generalist Combinatorial Optimization Agent Learner
Language-Informed Visual Concept Learning
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
PolaFormer: Polarity-aware Linear Attention for Vision Transformers
Exploring Diffusion Time-steps for Unsupervised Representation Learning
Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networks
Controlling Space and Time with Diffusion Models
It Helps to Take a Second Opinion: Teaching Smaller LLMs To Deliberate Mutually via Selective Rationale Optimisation
Finding Actual Descent Directions for Adversarial Training
Progressive Compression with Universally Quantized Diffusion Models
5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity
Scaling Laws for Neural Machine Translation
Accelerated Sampling with Stacked Restricted Boltzmann Machines
OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning
VC dimension of partially quantized neural networks in the overparametrized regime
Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
BrainLM: A foundation model for brain activity recordings
Understanding the Effectiveness of Cross-Domain Contrastive Unsupervised Domain Adaptation
PEER: A Collaborative Language Model
DynamicPoseNet: Advanced Human Motion Generation with Dual-Pathway CNNs and LoRA-Enhanced LLaMA
Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective
AffineQuant: Affine Transformation Quantization for Large Language Models
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
PowerQuant: Automorphism Search for Non-Uniform Quantization
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models
Re-evaluating Open-ended Evaluation of Large Language Models
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Do LLMs "know" internally when they follow instructions?
Exploring GPT-4 Vision for Text-to-Image Synthesis Evaluation
Learning Task Decomposition with Ordered Memory Policy Network
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks
Learning multi-scale local conditional probability models of images
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Meta-Learning with Fewer Tasks through Task Interpolation
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
RegionViT: Regional-to-Local Attention for Vision Transformers
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning
Extracting Robust Models with Uncertain Examples
FedImpro: Measuring and Improving Client Update in Federated Learning
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
The Hidden Language of Diffusion Models
Do as We Do, Not as You Think: the Conformity of Large Language Models
Easing Training Process of Rectified Flow Models Via Lengthening Inter-Path Distance
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Neural Graph Evolution: Towards Efficient Automatic Robot Design
Model Based Reinforcement Learning for Atari
Fostering Effective Communication Between Humans and Machines
6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
On Pre-training Language Model for Antibody
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
Massively Scaling Heteroscedastic Classifiers
Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
Bridging the Gap to Real-World Object-Centric Learning
Squeeze Training for Adversarial Robustness
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models
Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization
Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
What graph neural networks cannot learn: depth vs width
SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour
Multiple Source Domain Adaptation with Adversarial Learning
Light Sampling Field and BRDF Representation for Physically-based Neural Rendering
MixKD: Towards Efficient Distillation of Large-scale Language Models
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Language Agents Meet Causality - Bridging LLMs and Causal World Models
h4rm3l: A Language for Composable Jailbreak Attack Synthesis
Vision Transformer Adapter for Dense Predictions
Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
JoMA: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Distributional Reinforcement Learning with Monotonic Splines
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption
Disentangled Representation Learning with the Gromov-Monge Gap
Reducing the Effect of Incomplete Annotations in Object Detection for Histopathology
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts
A View From Somewhere: Human-Centric Face Representations
ScImage: How good are multimodal large language models at scientific text-to-image generation?
UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation
Improved algorithm and bounds for successive projection
Neural Optimal Transport with General Cost Functionals
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Learning to design protein-protein interactions with enhanced generalization
Theoretical bounds on estimation error for meta-learning
Knowledge Distillation of BERT Language Model on the Arabic Language
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
RMB: Comprehensively benchmarking reward models in LLM alignment
ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Efficient and Accurate Explanation Estimation with Distribution Compression
Training Free Guided Flow-Matching with Optimal Control
Eigenoption Discovery through the Deep Successor Representation
Probabilistic Implicit Scene Completion
Auction Learning as a Two-Player Game
Smoothness and Stability in GANs
The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
Interpreting and Boosting Dropout from a Game-Theoretic View
Neural SDF Flow for 3D Reconstruction of Dynamic Scenes
ImageFolder: Autoregressive Image Generation with Folded Tokens
Adaptive Brain Network Augmentation Based on Group-aware Graph Learning
Large Language Model Cascades with Mixture of Thought Representations for Cost-Efficient Reasoning
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
Context is Environment
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
Bidirectional Attention Flow for Machine Comprehension
Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution
Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models
Causal Information Prioritization for Efficient Reinforcement Learning
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
RaPP: Novelty Detection with Reconstruction along Projection Pathway
Combining Physics and Machine Learning for Network Flow Estimation
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
A General Rank Preserving Framework for Asymmetric Image Retrieval
Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions
Soft Contrastive Learning for Time Series
CABINET: Content Relevance-based Noise Reduction for Table Question Answering
Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Infinite-Horizon Differentiable Model Predictive Control
TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models
Human alignment of neural network representations
How many samples are needed to train a deep neural network?
Understanding Factual Recall in Transformers via Associative Memories
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions
Noise-free Score Distillation
Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos
No Preference Left Behind: Group Distributional Preference Optimization
FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
Open-Set Recognition: A Good Closed-Set Classifier is All You Need
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
Accuracy of white box and black box adversarial attacks on a sign activation 01 loss neural network ensemble
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure
On the Provable Advantage of Unsupervised Pretraining
Secure Communication Model for Quantum Federated Learning: A Proof of Concept
Improving Generalization and Stability of Generative Adversarial Networks
To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing
ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
Graph Parsing Networks
Metamizer: A Versatile Neural Optimizer for Fast and Accurate Physics Simulations
Generalization through variance: how noise shapes inductive biases in diffusion models
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models
TASAR: Transfer-based Attack on Skeletal Action Recognition
Anytime Minibatch: Exploiting Stragglers in Online Distributed Optimization
Lean-STaR: Learning to Interleave Thinking and Proving
Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
Amortised Invariance Learning for Contrastive Self-Supervision
Distributionally Robust Models with Parametric Likelihood Ratios
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
Mayfly: a Neural Data Structure for Graph Stream Summarization
Creative Sketch Generation
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Prototypical Contrastive Learning of Unsupervised Representations
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics
Factor Graph-based Interpretable Neural Networks
Equivariant Neural Functional Networks for Transformers
DEEM: Diffusion models serve as the eyes of large language models for image perception
Fantastic Copyrighted Beasts and How (Not) to Generate Them
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Maximum n-times Coverage for Vaccine Design
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Online Stabilization of Spiking Neural Networks
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Dynamic Neural Program Embeddings for Program Repair
Learning Deep Mean Field Games for Modeling Large Population Behavior
LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses
MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
Convergence of Bayesian Bilevel Optimization
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
DoF: A Diffusion Factorization Framework for Offline Multi-Agent Reinforcement Learning
Message Passing Neural PDE Solvers
Emergent Tool Use From Multi-Agent Autocurricula
u-μP: The Unit-Scaled Maximal Update Parametrization
Accelerating Neural ODEs: A Variational Formulation-based Approach
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Adversarial Latent Feature Augmentation for Fairness
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space
Spectral Embedding of Regularized Block Models
SVDQuant: Absorbing Outliers by Low-Rank Component for 4-Bit Diffusion Models
De novo drug design with deep generative models : an empirical study
Few-Shot Learning on graphs via super-Classes based on Graph spectral Measures
CPR: Classifier-Projection Regularization for Continual Learning
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Learning Features with Parameter-Free Layers
Interpretable Meta-Learning of Physical Systems
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
Simple yet Effective Incomplete Multi-view Clustering: Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction
Practical lossless compression with latent variables using bits back coding
Uncovering Overfitting in Large Language Model Editing
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
DDA: A dual-domain attention plug-and-play prior for pansharpening
Adversarial Imitation Learning via Boosting
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Denoising Diffusion Samplers
Variational Recurrent Models for Solving Partially Observable Control Tasks
Bandit Learning in Matching Markets with Indifference
Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons
Energy-based Generative Adversarial Networks
Evaluating Groups of Features via Consistency, Contiguity, and Stability
Using vision transformer-based GANs against Vision Transformers
Multi-Scale Representations by Varying Window Attention for Semantic Segmentation
A Closer Look at Machine Unlearning for Large Language Models
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
SAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site Detection
Diffusion-based Neural Network Weights Generation
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Stable Opponent Shaping in Differentiable Games
Learning on Large-scale Text-attributed Graphs via Variational Inference