yusuke1997/dblp-id-title huggingface dataset:
Learning Spatial-Semantic Features for Robust Video Object Segmentation
SEBRA : Debiasing through Self-Guided Bias Ranking
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
GOttack: Universal Adversarial Attacks on Graph Neural Networks via Graph Orbits Learning
Few-Shot Backdoor Attacks on Visual Object Tracking
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Sharpness-Aware Minimization: General Analysis and Improved Rates
MatPropXtractor: Generate to Extract
Exponential Machines
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
PubDef: Defending Against Transfer Attacks From Public Models
CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
Defensive Quantization: When Efficiency Meets Robustness
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
E(n) Equivariant Topological Neural Networks
Open-World Reinforcement Learning over Long Short-Term Imagination
Distributionally Robust Fair Principal Components via Geodesic Descents
Parallel Deep Neural Networks Have Zero Duality Gap
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
Distributionally Robust Post-hoc Classifiers under Prior Shifts
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
Statistical Methods for Auditing the Quality of Manual Content Reviews
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
BP-Modified Local Loss for Efficient Training of Deep Neural Networks
DynaPrompt: Dynamic Test-Time Prompt Tuning
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
The Evolution of Uncertainty of Learning in Games
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
Language-Agnostic Representation Learning of Source Code from Structure and Context
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
How robust is unsupervised representation learning to distribution shift?
On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning
Implicit regularization of deep residual networks towards neural ODEs
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
INFER: A Neural-symbolic Model For Extrapolation Reasoning on Temporal Knowledge Graph
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
On Graph Neural Networks versus Graph-Augmented MLPs
Learning Implicitly Recurrent CNNs Through Parameter Sharing
Compression by the signs: distributed learning is a two-way street
Learning Finite State Representations of Recurrent Policy Networks
Entropy-based Activation Function Optimization: A Method on Searching Better Activation Functions
Data Distillation for extrapolative protein design through exact preference optimization
CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
Designing Mechanical Meta-Materials by Learning Equivariant Flows
T2V2: A Unified Non-Autoregressive Model for Speech Recognition and Synthesis via Multitask Learning
ZeroFlow: Scalable Scene Flow via Distillation
REFACTOR: Learning to Extract Theorems from Proofs
Learning from Imperfect Human Feedback: A Tale from Corruption-Robust Dueling
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples
Preference Optimization for Reasoning with Pseudo Feedback
Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization
Is Forgetting Less a Good Inductive Bias for Forward Transfer?
Gaussian Process Behaviour in Wide Deep Neural Networks
LDMIC: Learning-based Distributed Multi-view Image Coding
ZeRO++: Extremely Efficient Collective Communication for Large Model Training
Disentangling the Mechanisms Behind Implicit Regularization in SGD
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Differentiable Learning-to-Normalize via Switchable Normalization
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Diverse Projection Ensembles for Distributional Reinforcement Learning
Provably Efficient CVaR RL in Low-rank MDPs
Explanations of GNN on Evolving Graphs via Axiomatic Layer edges
Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality
Enhancing Cross-lingual Transfer by Manifold Mixup
Ensemble Distillation for Unsupervised Constituency Parsing
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Pix2seq: A Language Modeling Framework for Object Detection
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA
The Role of Coverage in Online Reinforcement Learning
Prototypical Contrastive Predictive Coding
Revisiting Convolution Architecture in the Realm of DNA Foundation Models
DepthFL : Depthwise Federated Learning for Heterogeneous Clients
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
Multi-Agent Reinforcement Learning for Coalitional Bargaining Games
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
3DGS-Drag: Dragging Gaussians for Intuitive Point-Based 3D Editing
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
Faster Algorithms for Structured Linear and Kernel Support Vector Machines
On Improving Adversarial Transferability of Vision Transformers
Evaluating Representations with Readout Model Switching
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
Post-hoc Reward Calibration: A Case Study on Length Bias
The KFIoU Loss for Rotated Object Detection
Learning Uncertainty for Unknown Domains with Zero-Target-Assumption
Understanding the effects of data parallelism and sparsity on neural network training
Accelerated Over-Relaxation Heavy-Ball Method: Achieving Global Accelerated Convergence with Broad Generalization
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
Distributional Adversarial Networks
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint
Realistic Evaluation of Semi-Supervised Learning Algorithms
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control
A Theory of Tournament Representations
Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
Language Models Are Implicitly Continuous
Short and Deep: Sketching and Neural Networks
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Not-So-Random Features
Gradient-Free Generation for Hard-Constrained Systems
Does Writing with Language Models Reduce Content Diversity?
Elliptic Loss Regularization
A Robust Method to Discover Causal or Anticausal Relation
Don't Use Large Mini-batches, Use Local SGD
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
Proper Laplacian Representation Learning
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
IV-mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
Fast is better than free: Revisiting adversarial training
Towards Meta-Pruning via Optimal Transport
Simple Parameter-free Self-attention Approximation
CircNet: Meshing 3D Point Clouds with Circumcenter Detection
No Double Descent in Self-Supervised Learning
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
The Intrinsic Dimension of Images and Its Impact on Learning
Support-set bottlenecks for video-text representation learning
LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement
SAS: Structured Activation Sparsification
Neural structure learning with stochastic differential equations
Transferable Perturbations of Deep Feature Distributions
The Logical Expressiveness of Graph Neural Networks
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Start Smart: Leveraging Gradients For Enhancing Mask-based XAI Methods
Learning the Positions in CountSketch
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
BadRobot: Jailbreaking Embodied LLM Agents in the Physical World
3D-Spatial Multimodal Memory
Don't stop me Now: Embedding based Scheduling for LLMS
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Differentially Private Adaptive Optimization with Delayed Preconditioners
Learning Neural PDE Solvers with Convergence Guarantees
To Code or Not To Code? Exploring Impact of Code in Pre-training
VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Racial and Gender Stereotypes Encoded Into CLIP Representations
Deep Learning For Symbolic Mathematics
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation
Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
Scaling physics-informed hard constraints with mixture-of-experts
Progressive Parameter Efficient Transfer Learning for Semantic Segmentation
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform
Transfer RL across Observation Feature Spaces via Model-Based Regularization
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
Non-Autoregressive Dialog State Tracking
Learning Mesh-Based Simulation with Graph Networks
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
ADMM for Nonconvex Optimization under Minimal Continuity Assumption
ReMatching Dynamic Reconstruction Flow
Simulating Action Dynamics with Neural Process Networks
Improving protein optimization with smoothed fitness landscapes
Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning
Rethinking Classifier Re-Training in Long-Tailed Recognition: Label Over-Smooth Can Balance
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
InstantPortrait: One-Step Portrait Editing via Diffusion Multi-Objective Distillation
Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
Near-Optimal Solutions of Constrained Learning Problems
Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats
SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting
This Looks Like It Rather Than That: ProtoKNN For Similarity-Based Classifiers
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
Unsupervised visualization of image datasets using contrastive learning
Learning Proximal Operators to Discover Multiple Optima
On Bias-Variance Alignment in Deep Models
Adversarial score matching and improved sampling for image generation
Context-aware Dynamic Pruning for Speech Foundation Models
Robust Scheduling with GFlowNets
Revisiting Over-smoothing in BERT from the Perspective of Graph
PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning
On the Price of Differential Privacy for Hierarchical Clustering
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
Graph-based Document Structure Analysis
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers?
BooookScore: A systematic exploration of book-length summarization in the era of LLMs
Universal Successor Features Approximators
Multimodal Patient Representation Learning with Missing Modalities and Labels
Mastering Memory Tasks with World Models
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Sparse components distinguish visual pathways & their alignment to neural networks
S-NeRF: Neural Radiance Fields for Street Views
Are More Layers Beneficial to Graph Transformers?
Neural Design for Genetic Perturbation Experiments
Efficient Training on Very Large Corpora via Gramian Estimation
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
Estimation of single-cell and tissue perturbation effect in spatial transcriptomics via Spatial Causal Disentanglement
Quasi-hyperbolic momentum and Adam for deep learning
Efficient Empowerment Estimation for Unsupervised Stabilization
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
In-sample Actor Critic for Offline Reinforcement Learning
Emergent Communication at Scale
On the Adversarial Vulnerability of Label-Free Test-Time Adaptation
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Geometry of Long-Tailed Representation Learning: Rebalancing Features for Skewed Distributions
Generalized Kernel Thinning
Exploring Low-Rank Property in Multiple Instance Learning for Whole Slide Image Classification
Robust Reinforcement Learning for Continuous Control with Model Misspecification
CLEVRER: Collision Events for Video Representation and Reasoning
Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering
MogaNet: Multi-order Gated Aggregation Network
CO2: Efficient Distributed Training with Full Communication-Computation Overlap
Re-parameterizing Your Optimizers rather than Architectures
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Towards Robust, Locally Linear Deep Networks
Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement
Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
4K4DGen: Panoramic 4D Generation at 4K Resolution
KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Process for Time Series Forecasting
Strategies for Pre-training Graph Neural Networks
Exact Certification of (Graph) Neural Networks Against Label Poisoning
RGB-Event ISP: The Dataset and Benchmark
Learning to Multi-Task by Active Sampling
Learning to Set Waypoints for Audio-Visual Navigation
P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding
Boosting Randomized Smoothing with Variance Reduced Classifiers
UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Contrastive Divergence Learning is a Time Reversal Adversarial Game
MIND: Math Informed syNthetic Dialogues for Pretraining LLMs
SimPer: Simple Self-Supervised Learning of Periodic Targets
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
Loss-Free Machine Unlearning
Equi-normalization of Neural Networks
Differentiable DAG Sampling
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
Captured by Captions: On Memorization and its Mitigation in CLIP Models
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Self-Joint Supervised Learning
Node-Time Conditional Prompt Learning in Dynamic Graphs
Learning Representations of Sets through Optimized Permutations
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
Elucidating the design space of classifier-guided diffusion generation
CityAnchor: City-scale 3D Visual Grounding with Multi-modality LLMs
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Knowledge distillation via softmax regression representation learning
Universalizing Weak Supervision
ViTGAN: Training GANs with Vision Transformers
Multi-Mention Learning for Reading Comprehension with Neural Cascades
CofCA: A STEP-WISE Counterfactual Multi-hop QA benchmark
Zero-Cost Proxies for Lightweight NAS
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
The Trickle-down Impact of Reward Inconsistency on RLHF
Grammar Reinforcement Learning: path and cycle counting in graphs with a Context-Free Grammar and Transformer approach
Is CLIP Fooled by Optical Illusions?
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Boosting the Actor with Dual Critic
Towards Synergistic Path-based Explanations for Knowledge Graph Completion: Exploration and Evaluation
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking
Provable Memorization Capacity of Transformers
CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning
Deep Kernel Machines via the Kernel Reparametrization Trick
A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
Iterative GANs for Rotating Visual Objects
RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
Model Zoo: A Growing Brain That Learns Continually
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
ConR: Contrastive Regularizer for Deep Imbalanced Regression
Decoupling Global and Local Representations via Invertible Generative Flows
Oscillatory State-Space Models
OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition
CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models
Whispering Across the Continent: Collecting and Analyzing African Culture Using Community Radios
DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Denoising Task Routing for Diffusion Models
CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
Sound Adversarial Audio-Visual Navigation
Filter shaping for Convolutional Neural Networks
A Discretization Framework for Robust Contextual Stochastic Optimization
Deep ReLU Networks Preserve Expected Length
On-the-fly Preference Alignment via Principle-Guided Decoding
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
The Labyrinth of Links: Navigating the Associative Maze of Multi-modal LLMs
Distilling Dataset into Neural Field
Diffusion Models for Causal Discovery via Topological Ordering
Towards Automated Knowledge Integration From Human-Interpretable Representations
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels
Demystifying MMD GANs
On the Bottleneck of Graph Neural Networks and its Practical Implications
Active Learning with Partial Feedback
Scaling Laws of RoPE-based Extrapolation
Topological Graph Neural Networks
Identifiable Latent Polynomial Causal Models through the Lens of Change
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Statistical Rejection Sampling Improves Preference Optimization
Learning to Embed Time Series Patches Independently
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
MIMT: Masked Image Modeling Transformer for Video Compression
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
Third Person Imitation Learning
Differentiable Integer Linear Programming
Representation Learning via Invariant Causal Mechanisms
Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
Collaborative Pure Exploration in Kernel Bandit
Cross-Domain Imitation Learning via Optimal Transport
Making Better Decision by Directly Planning in Continuous Control
On the Benefits of Memory for Modeling Time-Dependent PDEs
Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
Towards Unbiased Learning in Semi-Supervised Semantic Segmentation
Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing
Revisiting a Design Choice in Gradient Temporal Difference Learning
Attentional Constellation Nets for Few-Shot Learning
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds
H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts
Using latent space regression to analyze and leverage compositionality in GANs