yusuke1997/dblp-id-title huggingface dataset:
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding
Injecting Universal Jailbreak Backdoors into LLMs in Minutes
Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space
Taming Sparsely Activated Transformer with Stochastic Experts
How new data permeates LLM knowledge and how to dilute it
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Language Model Cascades: Token-Level Uncertainty And Beyond
Towards Variational Generation of Small Graphs
Visual Correspondence Hallucination
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
Incentive-Aware Federated Learning with Training-Time Model Rewards
MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Long-Tailed Partial Label Learning via Dynamic Rebalancing
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment
Efficient Learning rate schedules for Stochastic Non-negative Matrix Factorization via Reinforcement Learning
Adaptive Memory Networks
Data-dependent Gaussian Prior Objective for Language Generation
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Retro-fallback: retrosynthetic planning in an uncertain world
Data Debugging with Shapley Importance over Machine Learning Pipelines
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics
One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks
AnyDA: Anytime Domain Adaptation
Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model
QPM: Discrete Optimization for Globally Interpretable Image Classification
Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model
Augmented Bayesian Policy Search
Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features
Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns
Multi-View Causal Representation Learning with Partial Observability
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
Can LLMs Understand Time Series Anomalies?
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
HShare: Fast LLM Decoding by Hierarchical Key-Value Sharing
Global Optimality for Non-linear Constrained Restoration Problems via Invexity
MCAL: Minimum Cost Human-Machine Active Labeling
Radar: Fast Long-Context Decoding for Any Transformer
Bad Predictive Coding Activation Functions
The Art of Embedding Fusion: Optimizing Hate Speech Detection
OLMoE: Open Mixture-of-Experts Language Models
Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems
Selecting the Best in GANs Family: a Post Selection Inference Framework
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Noise against noise: stochastic label noise helps combat inherent label noise
Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games?
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
Generative Judge for Evaluating Alignment
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
EG4D: Explicit Generation of 4D Object without Score Distillation
Robustness Verification for Transformers
Information-Directed Exploration for Deep Reinforcement Learning
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Incentivized Truthful Communication for Federated Bandits
A Program to Build E(N)-Equivariant Steerable CNNs
What is Wrong with Perplexity for Long-context Language Modeling?
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
SBSC: Step-by-Step Coding for Improving Mathematical Olympiad Performance
pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Improving Deep Policy Gradients with Value Function Search
Neural Spectral Methods: Self-supervised learning in the spectral domain
FearNet: Brain-Inspired Model for Incremental Learning
Large Scale Graph Learning From Smooth Signals
Systematic Relational Reasoning With Epistemic Graph Neural Networks
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Boosting Vanilla Lightweight Vision Transformers via Re-parameterization
Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds
SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
From Variational to Deterministic Autoencoders
Discovering a set of policies for the worst case reward
Uncertainty Modeling for Out-of-Distribution Generalization
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
Robustness to corruption in pre-trained Bayesian neural networks
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Beyond Greedy Ranking: Slate Optimization via List-CVAE
Trusting SVM for Piecewise Linear CNNs
Palu: KV-Cache Compression with Low-Rank Projection
CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Generalizing Across Domains via Cross-Gradient Training
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Tight Time Complexities in Parallel Stochastic Optimization with Arbitrary Computation Dynamics
FACTS: A Factored State-Space Framework for World Modelling
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
Goodhart's Law in Reinforcement Learning
Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective
ComboGAN: Unrestricted Scalability for Image Domain Translation
Contrastive Learning is Spectral Clustering on Similarity Graph
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
Order Learning and Its Application to Age Estimation
Unlearnable Examples: Making Personal Data Unexploitable
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
Zero-shot generalization across architectures for visual classification
SafeDreamer: Safe Reinforcement Learning with World Models
LASER: A Neuro-Symbolic Framework for Learning Spatio-Temporal Scene Graphs with Weak Supervision
Words or Characters? Fine-grained Gating for Reading Comprehension
MINDE: Mutual Information Neural Diffusion Estimation
Mind's Eye: Grounded Language Model Reasoning through Simulation
Curriculum Loss: Robust Learning and Generalization against Label Corruption
TAPEX: Table Pre-training via Learning a Neural SQL Executor
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model
HyperGrid Transformers: Towards A Single Model for Multiple Tasks
Beyond Mere Token Analysis: A Hypergraph Metric Space Framework for Defending Against Socially Engineered LLM Attacks
Excessive Invariance Causes Adversarial Vulnerability
Discovering Invariant Rationales for Graph Neural Networks
Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits
Pre-training LiDAR-based 3D Object Detectors through Colorization
Extreme Classification via Adversarial Softmax Approximation
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication
Generating Diverse Cooperative Agents by Learning Incompatible Policies
Language Model Detectors Are Easily Optimized Against
Improving Out-of-distribution Generalization with Indirection Representations
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle
Grokking as a First Order Phase Transition in Two Layer Networks
Supervised and Semi-Supervised Diffusion Maps with Label-Driven Diffusion
Directly Fine-Tuning Diffusion Models on Differentiable Rewards
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Node Similarities under Random Projections: Limits and Pathological Cases
Residual Connections Encourage Iterative Inference
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
Pushing Boundaries: Mixup's Influence on Neural Collapse
Unseen Style Transfer Based on a Conditional Fast Style Transfer Network
Learned Simulators for Turbulence
Neural Eulerian Scene Flow Fields
Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Planning with Sequence Models through Iterative Energy Minimization
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers
Pre-Training and Fine-Tuning Generative Flow Networks
HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models
Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation
PolyVoice: Language Models for Speech to Speech Translation
Learning proposals for sequential importance samplers using reinforced variational inference
sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
Neural Radiance Field Codebooks
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
Tuning Frequency Bias in Neural Network Training with Nonuniform Data
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
On Compositional Uncertainty Quantification for Seq2seq Graph Parsing
High-dimension Prototype is a Better Incremental Object Detection Learner
Fast convergence of stochastic subgradient method under interpolation
Embracing Data Abundance
Image BERT Pre-training with Online Tokenizer
You Only Query Once: An Efficient Label-Only Membership Inference Attack
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
Modelling complex vector drawings with stroke-clouds
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Explainable Deep One-Class Classification
Graph Condensation for Graph Neural Networks
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning
Demystifying Embedding Spaces using Large Language Models
Knowledge Removal in Sampling-based Bayesian Inference
The Duality of Hope: A Critical Examination of Controversial Annotations in HopeEDI
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks
A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles
Learning Manifold Patch-Based Representations of Man-Made Shapes
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
Ensembles of Low-Rank Expert Adapters
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Drowning Detection based on YOLOv8 improved by GP-GAN Augmentation
𝒪-GNN: incorporating ring priors into molecular modeling
Nonseparable Symplectic Neural Networks
Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism
Neural Delay Differential Equations
On the Dynamics of Training Attention Models
Robust Transfer of Safety-Constrained Reinforcement Learning Agents
A Probabilistic Framework for Modular Continual Learning
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
Efficient Vector Representation for Documents through Corruption
Towards Realistic Data Generation for Real-World Super-Resolution
Skill Expansion and Composition in Parameter Space
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
Fitting Networks with a Cancellation Trick
$\Lambda$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach
Optimal robust Memorization with ReLU Neural Networks
SUDANESE ARABIC DIALECT ENCODING USING XLM-RoBERTa LANGUAGE MODEL: Zol-ROBERTA
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models
DeLighT: Deep and Light-weight Transformer
Improved Training Technique for Latent Consistency Models
AutoLoRa: An Automated Robust Fine-Tuning Framework
An Investigation of Representation and Allocation Harms in Contrastive Learning
Parameter Efficient Multimodal Transformers for Video Representation Learning
Marginalized Average Attentional Network for Weakly-Supervised Learning
LSTM Iteration Networks: An Exploration of Differentiable Path Finding
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
LLaMA-Omni: Seamless Speech Interaction with Large Language Models
Hyperparameter Tuning with Renyi Differential Privacy
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization
Variational Latent Branching Model for Off-Policy Evaluation
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
Noisy Networks For Exploration
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Neural Neighborhood Search for Multi-agent Path Finding
Near-optimal Active Regression of Single-Index Models
Autoregressive Conditional Neural Processes
Uncovering Latent Memories in Large Language Models
Attention-based Iterative Decomposition for Tensor Product Representation
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation
Prior knowledge meets Neural ODEs: a two-stage training method for improved explainability
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
Mixed Precision DNNs: All you need is a good parametrization
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Policy Smoothing for Provably Robust Reinforcement Learning
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
When does preconditioning help or hurt generalization?
Intensity-Free Learning of Temporal Point Processes
Local convergence of simultaneous min-max algorithms to differential equilibrium on Riemannian manifold
Visual Semantic Navigation using Scene Priors
Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Adaptive Path-Integral Approach for Representation Learning and Planning
𝒩-WL: A New Hierarchy of Expressivity for Graph Neural Networks
Mirostat: a Neural Text decoding Algorithm that directly controls perplexity
Segment Any 3D Object with Language
On Error Propagation of Diffusion Models
SCALOR: Generative World Models with Scalable Object Representations
SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation
AmbientGAN: Generative models from lossy measurements
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution
SEED: Self-supervised Distillation For Visual Representation
Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
MaRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
LLMs Can Plan Only If We Tell Them
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
Wasserstein Distances, Neuronal Entanglement, and Sparsity
Revisiting the Assumption of Latent Separability for Backdoor Defenses
Evaluating Large Language Models at Evaluating Instruction Following
Unsupervised Model Selection for Time Series Anomaly Detection
Visual Reasoning by Progressive Module Networks
Learning to Link
Learning and Evaluating Representations for Deep One-Class Classification
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
TransNets for Review Generation
The Effectiveness of Random Forgetting for Robust Generalization
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
Maximum a Posteriori Policy Optimisation
On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training
Neural networks on Symmetric Spaces of Noncompact Type
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
On the Robustness of Drug Abuse Face Classification
Learning to Act without Actions
Some Fundamental Aspects about Lipschitz Continuity of Neural Networks
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Compositional Kernel Machines
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
To the Cutoff... and Beyond? A Longitudinal Perspective on LLM Data Contamination
Symplectic Recurrent Neural Networks
Nearest Neighbor Machine Translation
Learning mirror maps in policy mirror descent
STAR: Stability-Inducing Weight Perturbation for Continual Learning
ARGS: Alignment as Reward-Guided Search
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
Instant Policy: In-Context Imitation Learning via Graph Diffusion
Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making
Interpreting Robustness Proofs of Deep Neural Networks
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems
Functional vs. parametric equivalence of ReLU networks
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
It Takes Two to Tango: Mixup for Deep Metric Learning
Towards Foundation Models for Mixed Integer Linear Programming
Understanding the Effects of RLHF on LLM Generalisation and Diversity
Generative AI for Therapy? Opportunities and Barriers for ChatGPT in Speech-Language Therapy
Unsupervised Adversarial Image Reconstruction
Distance-Based Tree-Sliced Wasserstein Distance
A law of adversarial risk, interpolation, and label noise
Robustness May Be at Odds with Accuracy
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Semiparametric Reinforcement Learning
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
Chain Of Thought Prompting Under Streaming Batch: A Case Study
Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures
A 2-Dimensional State Space Layer for Spatial Inductive Bias
Pretrained Language Model in Continual Learning: A Comparative Study
Real-Time Video Generation with Pyramid Attention Broadcast
A moth brain learns to read MNIST
The Curious Case of Neural Text Degeneration
Utilizing Cross-Version Consistency for Domain Adaptation: A Case Study on Music Audio
Layer-adaptive Sparsity for the Magnitude-based Pruning
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
On Linear Representations and Pretraining Data Frequency in Language Models
CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding
Spatial Attention Kinetic Networks with E(n)-Equivariance
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
Proximal Backpropagation
Online Learning Rate Adaptation with Hypergradient Descent
Learning to CROSS exchange to solve min-max vehicle routing problems
Towards Deep Learning Models Resistant to Adversarial Attacks
A Pre-Search Evaluation Framework for Assessing Search Space Complexity
Emergent Orientation Maps - - Mechanisms, Coding Efficiency and Robustness
Private Mechanism Design via Quantile Estimation
Designing Efficient Neural Attention Systems Towards Achieving Human-level Sharp Vision
Dissecting Adversarial Robustness of Multimodal LM Agents
TrojText: Test-time Invisible Textual Trojan Insertion
Neural Arithmetic Units
Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
Query Embedding on Hyper-Relational Knowledge Graphs
Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Autonomous Learning of Object-Centric Abstractions for High-Level Planning
An image representation based convolutional network for DNA classification
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection