yusuke1997/dblp-id-title huggingface dataset:
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
Joint Embeddings of Scene Graphs and Images
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Explaining Time Series via Contrastive and Locally Sparse Perturbations
HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?
Visual Data-Type Understanding does not emerge from scaling Vision-Language Models
Online Low Rank Matrix Completion
Episodic Novelty Through Temporal Distance
Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
Leveraging unlabeled data to predict out-of-distribution performance
MMEgo: Towards Building Egocentric Multimodal LLMs for Video QA
OpenPRM: Building Open-domain Process-based Reward Models with Preference Trees
Audio Large Language Models Can Be Descriptive Speech Quality Evaluators
Time to augment self-supervised visual representation learning
Exploring The Forgetting in Adversarial Training: A Novel Method for Enhancing Robustness
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Exploration by random network distillation
Charting the Design Space of Neural Graph Representations for Subgraph Matching
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
Quadtree Attention for Vision Transformers
Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Understanding Knowledge Distillation in Non-autoregressive Machine Translation
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling
What Makes a Good Diffusion Planner for Decision Making?
Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling, and Zero-Shot Transfer
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs
Transferring Knowledge to Smaller Network with Class-Distance Loss
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Compositional Training for End-to-End Deep AUC Maximization
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Feature-Based Online Bilateral Trade
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Circuit Component Reuse Across Tasks in Transformer Language Models
Phase transition for detecting a small community in a large network
Learning Parametric Closed-Loop Policies for Markov Potential Games
MiniPLM: Knowledge Distillation for Pre-training Language Models
Associative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-based Perception
A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming
EmbedLLM: Learning Compact Representations of Large Language Models
Learning and aligning single-neuron invariance manifolds in visual cortex
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess
Doc2Command: Furthering Language Guided Document Editing
Scaling Long Context Training Data by Long-Distance Referrals
Sensor-Invariant Tactile Representation
Making Text Embedders Few-Shot Learners
Learning One-hidden-layer Neural Networks with Landscape Design
FairCal: Fairness Calibration for Face Verification
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes
Optimal Completion Distillation for Sequence Learning
A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation
SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
Efficient Sparse PCA via Block-Diagonalization
Et-Seed: Efficient trajectory-Level SE(3) equivariant diffusion Policy
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
StrokeNet: A Neural Painting Environment
Conformal Risk Control
Repulsive Latent Score Distillation for Solving Inverse Problems
Training GANs with Stronger Augmentations via Contrastive Discriminator
Pruning Convolutional Neural Networks for Resource Efficient Inference
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
The Obscure Limitation of Modular Multilingual Language Models
Learning Programmatically Structured Representations with Perceptor Gradients
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Can Neural Networks Understand Logical Entailment?
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
Improving generalization by loss modification
Mutual Information Gradient Estimation for Representation Learning
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
Task structure and nonlinearity jointly determine learned representational geometry
Point-SAM: Promptable 3D Segmentation Model for Point Clouds
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Design of a molecular exchange-based robust perceptron for biomolecular neural network
Neural Fluid Simulation on Geometric Surfaces
Discovering Latent Concepts Learned in BERT
Covariant Compositional Networks For Learning Graphs
Compositional simulation-based inference for time series
PAC Reinforcement Learning for Predictive State Representations
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Learning Chaos In A Linear Way
Efficient Neuron Segmentation in Electron Microscopy by Affinity-Guided Queries
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability
Disagreement-Regularized Imitation Learning
MoReL: Multi-omics Relational Learning
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Bonsai: Gradient-free Graph Condensation for Node Classification
AmortizedPeriod: Attention-based Amortized Inference for Periodicity Identification
Procedural generalization by planning with self-supervised world models
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Doubly Robust Proximal Causal Learning for Continuous Treatments
Provable unlearning in topic modeling and downstream tasks
Random-Set Neural Networks
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
Diffusion Models as Cartoonists: The Curious Case of High Density Regions
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
Learning Nash Equilibria in Rank-1 Games
Twin Networks: Matching the Future for Sequence Generation
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
SIMPLE: A Gradient Estimator for k-Subset Sampling
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment
Multitask Prompted Training Enables Zero-Shot Task Generalization
Relational Surrogate Loss Learning
Visually Consistent Hierarchical Image Classification
Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning
Transformer-based World Models Are Happy With 100k Interactions
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Test-time Adaptation for Image Compression with Distribution Regularization
RA-NER: Retrieval augmented NER for knowledge intensive named entity recognition
Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions
Durable Quantization Conditioned Misalignment Attack on Large Language Models
PDE-Driven Spatiotemporal Disentanglement
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
MA2E: Addressing Partial Observability in Multi-Agent Reinforcement Learning with Masked Auto-Encoder
Proxy Denoising for Source-Free Domain Adaptation
Semi-Implicit Variational Inference via Score Matching
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Gradient descent with generalized Newton's method
CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos
6D Object Pose Tracking in Internet Videos for Robotic Manipulation
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification
Understanding the Role of Self Attention for Efficient Speech Recognition
Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models
Privacy-Aware Lifelong Learning
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Self-Attention-Based Contextual Modulation Improves Neural System Identification
Learning Optimal Contracts: How to Exploit Small Action Spaces
Is DFR for Soft Biometrics Prediction in Unconstrained Images Fair and Effective?
Multilevel Generative Samplers for Investigating Critical Phenomena
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Dynamic Activations for Neural Net Training
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
Linear Mode Connectivity in Multitask and Continual Learning
eCommerceGAN: A Generative Adversarial Network for e-commerce
Is Conditional Generative Modeling all you need for Decision Making?
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
Reconciling Spatial and Temporal Abstractions for Goal Representation
A Non-Parametric Regression Viewpoint : Generalization of Overparametrized Deep RELU Network Under Noisy Observations
Variational Predictive Routing with Nested Subjective Timescales
Uncertainty-aware Active Learning for Optimal Bayesian Classifier
Unsupervised Discovery of Object Radiance Fields
The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Molecule Optimization by Explainable Evolution
Principled Weight Initialization for Hypernetworks
Fengbo: a Clifford Neural Operator pipeline for 3D PDEs in Computational Fluid Dynamics
An Experimental Study of Neural Networks for Variable Graphs
Learning Video-Conditioned Policy on Unlabelled Data with Joint Embedding Predictive Transformer
Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs
Self-Supervision Enhanced Feature Selection with Correlated Gates
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
t-Divergence: A New Divergence Measure with Application to Robust Statistics & Clustering
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
Strength of Minibatch Noise in SGD
Complexity-Based Prompting for Multi-step Reasoning
Unrestricted Adversarial Examples via Semantic Manipulation
Sequential Attention for Feature Selection
Spacetime Representation Learning
Aggregated Momentum: Stability Through Passive Damping
Controlled Text Generation via Language Model Arithmetic
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient
QuAnt: Quantum Annealing with Learnt Couplings
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Support is All You Need for Certified VAE Training
Scalable Neural Network Kernels
PiFold: Toward effective and efficient protein inverse folding
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Monitoring Latent World States in Language Models with Propositional Probes
Meta-learning curiosity algorithms
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis
A Mean Field Theory of Batch Normalization
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Systematic generalisation with group invariant predictions
Towards Synthesizing Complex Programs From Input-Output Examples
Value Gradient weighted Model-Based Reinforcement Learning
Self-training For Few-shot Transfer Across Extreme Task Differences
EqNIO: Subequivariant Neural Inertial Odometry
Classic but Everlasting: Traditional Gradient-Based Algorithms Converge Fast Even in Time-Varying Multi-Player Games
AdaSpeech: Adaptive Text to Speech for Custom Voice
WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding
Information Theoretic lower bounds on negative log likelihood
Generative Modeling for Protein Structures
The role of Disentanglement in Generalisation
Discrete Distribution Networks
RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
QP-SNN: Quantized and Pruned Spiking Neural Networks
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Generating Adversarial Computer Programs using Optimized Obfuscations
Dual Student Networks for Data-Free Model Stealing
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Compositional Generative Inverse Design
Understanding the failure modes of out-of-distribution generalization
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Normalization of Language Embeddings for Cross-Lingual Alignment
Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
C-CLIP: Multimodal Continual Learning for Vision-Language Model
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
Oracle Efficient Algorithms for Groupwise Regret
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
Emergence of Maps in the Memories of Blind Navigation Agents
Scale-aware Recognition in Satellite Images under Resource Constraints
DFWLayer: Differentiable Frank-Wolfe Optimization Layer
SEARNN: Training RNNs with global-local losses
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Faster Binary Embeddings for Preserving Euclidean Distances
Capturing Label Characteristics in VAEs
Prototypical Representation Learning for Relation Extraction
Understanding DDPM Latent Codes Through Optimal Transport
Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting
Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities
Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates
Exploring the Design Space of Visual Context Representation in Video MLLMs
Hyperparameter optimization: a spectral approach
DictFormer: Tiny Transformer with Shared Dictionary
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
Personalize Segment Anything Model with One Shot
Scaling Laws for Sparsely-Connected Foundation Models
On Distributed Adaptive Optimization with Gradient Compression
Unified Parameter-Efficient Unlearning for LLMs
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Optimization as a Model for Few-Shot Learning
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
Characters Are Like Faces
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
Quasi-Recurrent Neural Networks
Towards Robust Object Detection Invariant to Real-World Domain Shifts
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing
An efficient framework for learning sentence representations
Behaviour Suite for Reinforcement Learning
Training-Free Diffusion Model Alignment with Sampling Demons
CONGO: Compressive Online Gradient Optimization
Scalable Monotonic Neural Networks
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions
StringLLM: Understanding the String Processing Capability of Large Language Models
Smooth markets: A basic mechanism for organizing gradient-based learners
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Building Generalizable Agents with a Realistic and Rich 3D Environment
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
A Simple Loss Function for Convergent Algorithm Synthesis using RNNs
Systems with Switching Causal Relations: A Meta-Causal Perspective
On Unifying Deep Generative Models
Autoregressive Generative Adversarial Networks
Spectrally Transformed Kernel Regression
Implicit Neural Representations and the Algebra of Complex Wavelets
Meta-Learning a Dynamical Language Model
Token-Supervised Value Models for Enhancing Mathematical Problem-Solving Capabilities of Large Language Models
Step-unrolled Denoising Autoencoders for Text Generation
Learning Robust Representations by Projecting Superficial Statistics Out
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
Accelerating SGD with momentum for over-parameterized learning
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
Diffusion Models Already Have A Semantic Latent Space
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
Influencer Backdoor Attack on Semantic Segmentation
Aligning Relational Learning with Lipschitz Fairness
Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement
Distributed Second-Order Optimization using Kronecker-Factored Approximations
Adversarial Spheres
Nonlinear Acceleration of CNNs
Neuron as an Agent
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
Once-for-All: Train One Network and Specialize it for Efficient Deployment
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Causal Imitation Learning via Inverse Reinforcement Learning
HeadMap: Locating and Enhancing Knowledge Circuits in LLMs
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling
LogicDP: Creating Labels for Graph Data via Inductive Logic Programming
Adversarial Attacks on Data Attribution
Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams
Can Textual Gradient Work in Federated Learning?
CryoFM: A Flow-based Foundation Model for Cryo-EM Densities
Can Knowledge Editing Really Correct Hallucinations?
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Lossless Compression with Probabilistic Circuits
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Performance Evaluation of Enhanced ConvNeXtTiny-based Fire Detection System in Real-world Scenarios
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Unsupervised Disentanglement with Tensor Product Representations on the Torus
Point-based Instance Completion with Scene Constraints
Robust and Controllable Object-Centric Learning through Energy-based Models
Querying Easily Flip-flopped Samples for Deep Active Learning
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
Exact Community Recovery under Side Information: Optimality of Spectral Algorithms
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$
Finite Depth and Width Corrections to the Neural Tangent Kernel
SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddings
A Brief History of the Speculative Measures for Autonomy
Weak-to-Strong Generalization Through the Data-Centric Lens
Standardizing Structural Causal Models