3d ago
Task-Distributionally Robust Data-Free Meta-Learning
Data-free meta-learning robustness analysis examining failure modes when learning from pre-trained models without training data.
3d ago
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
MARL method using temporal sparse coordination graphs to improve agent cooperation from historical experiences.
3d ago
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning coordination via graph structures capturing higher-order group relationships.
3d ago
Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
Self-supervised learning for ECG signal representation using masked modeling in medical domain.
3d ago
FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening
GNN scalability method using graph coarsening to reduce inference-time computational costs.
3d ago
Graph Defense Diffusion Model
Diffusion model approach for defending graph neural networks against adversarial attacks.
3d ago
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Graph neural network architecture using Mamba state space models to address over-smoothing in deep GNNs.
3d ago
Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Method using low-rank techniques for Bayesian uncertainty quantification in neural networks via Laplace approximation.
3d ago
Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Research on polysemanticity in LLMs showing neurons encode multiple concepts, challenging discrete attribution methods for model interpretability.
3d ago
Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling
Research on reducing class bias in balanced datasets using hardness-based resampling instead of frequency-based methods.
3d ago
Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Federated continual fine-tuning with low-rank residual adaptation, enabling efficient parameter-efficient learning across new classes in federated settings.
3d ago
Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models
Proxy model framework for efficient post-hoc interpretability of LLMs, reducing computational costs of model-agnostic explanations.
3d ago
Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Theoretical analysis of OPTQ/GPTQ post-training quantization for LLMs, providing rigorous quantitative guarantees for PTQ algorithms.
3d ago
AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting
Kolmogorov-Arnold networks with autoregressive weights for time series forecasting, extending comparisons beyond LLMs and FNNs.
3d ago
STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
Spatial-temporal weather forecasting with adaptive boundary alignment for regional integration from global atmosphere predictions.
3d ago
On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
Configuration-aware LoRA adaptation for quantized LLMs enabling efficient edge device deployment with heterogeneous capabilities.
3d ago
Large Reasoning Models Learn Better Alignment from Flawed Thinking
RECAP: RL method for safety alignment in large reasoning models, teaching critical evaluation of flawed premises via counter-aligned prefilling.
3d ago
Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Open dataset of batch distillation experiments for developing ML anomaly detection methods in chemical processes.
3d ago
Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
Machine learning models for metabolic liver disease prediction from EHR data, comparing LASSO, random forests, and neural networks.
3d ago
LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation
LLM-based flight delay prediction integrating textual aeronautical data and aircraft trajectories for air traffic management.
3d ago
Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling
Graph neural network architecture using selective state space modeling to address over-smoothing in deep GNNs via node-specific representation evolution.
3d ago
Boosting Brain-inspired Path Integration Efficiency via Learning-based Replication of Continuous Attractor Neurodynamics
Optimization of continuous attractor neural networks for brain-inspired path integration, reducing computational redundancy in navigation systems.
3d ago
AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
Vision-Language-Action model with active visual attention for robotic manipulation, extending from Markov to partially observable decision processes.
3d ago
From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
Analysis of flow-based diffusion models revealing two-stage behavior through oracle velocity fields, focusing on memorization-generalization dynamics.
3d ago
Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning
Multi-agent RL framework for adaptive traffic signal control, replacing static controllers with learning-based optimization for complex traffic dynamics.
3d ago
Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
Multi-agent RL for graph-based coordination with bandwidth constraints, addressing what information agents should transmit under communication limits.
3d ago
The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs
Analysis of self-reflection emergence in LLMs through RL post-training, using gradient attribution to explain distinct solution generation and revision capabilities.
3d ago
Imitation Learning for Combinatorial Optimisation under Uncertainty
Imitation learning framework for combinatorial optimization problems, examining how expert demonstrations affect policy learning in sequential decision problems.
3d ago
FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning
FP8 low-precision quantization for LLM reinforcement learning, addressing memory and compute bottlenecks in rollout generation with engineering and algorithmic solutions.
3d ago
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
Demonstrates layer pruning limitations for LLM reasoning tasks, showing pruned models lose algorithmic capabilities despite compression on classification tasks.
3d ago
dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning
dnaHNet foundation model for genomic sequence learning with tokenizer-free design preserving biological motifs while handling long contexts efficiently.
3d ago
An Adaptive Model Selection Framework for Demand Forecasting under Horizon-Induced Degradation to Support Business Strategy and Operations
Adaptive model selection framework for demand forecasting addressing horizon-induced degradation across heterogeneous inventory portfolios.
3d ago
TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction
Physics-guided neural network for high-resolution air quality prediction incorporating topography and wind direction as critical factors.
3d ago
SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
SubQuad pipeline for adaptive immune repertoire analysis combining subquadratic retrieval with learned multimodal fusion for clinical clonotype detection.
3d ago
Reinforcement-aware Knowledge Distillation for LLM Reasoning
Reinforcement-aware knowledge distillation method for distilling RL-trained reasoning LLMs into smaller models while preserving chain-of-thought capability.
3d ago
Accelerating Local LLMs on Resource-Constrained Edge Devices via Distributed Prompt Caching
Distributed prompt caching technique for accelerating local LLM inference on resource-constrained edge devices via inter-device state sharing.
3d ago
Implicit Bias in Deep Linear Discriminant Analysis
Analyzes implicit regularization of Deep LDA objective for scale-invariant discriminative metric learning.
3d ago
Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails
Theoretical analysis explaining Adam's empirical advantage over SGD through second-moment normalization using stopping-time/martingale analysis.
3d ago
Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAX
Enables exact gradient computation for spiking neural networks via differentiable ODE solving in JAX, supporting arbitrary neuron models.
3d ago
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
Proposes prototypical exemplar condensation for memory-efficient continual learning, reducing stored samples per class from 20+ to single digits.
3d ago
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
ALMAB-DC framework combines active learning, multi-armed bandits, and distributed computing for expensive black-box optimization.
3d ago
Mechanisms of Introspective Awareness
Investigates mechanisms of introspective awareness in LLMs, where models detect injected steering vectors with minimal false positives.
3d ago
Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
Adapts KGE metric for non-stationary geoscientific systems in water management applications.
3d ago
Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications
Analyzes distributional reinforcement learning with applications to healthcare, moving beyond expectation-based objectives for uncertain domains.
3d ago
Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
Proposes hierarchical SVG tokenization approach for improved scalable vector graphics modeling with LLMs via geometric-aware token design.
3d ago
ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
ALTO system for adaptive hyperparameter tuning and orchestration of LoRA fine-tuning jobs across heterogeneous multi-tenant environments.
3d ago
Gated-SwinRMT: Unifying Swin Windowed Attention with Retentive Manhattan Decay via Input-Dependent Gating
Introduces Gated-SwinRMT, a vision transformer combining Swin attention with Manhattan-distance decay for improved spatial modeling.
3d ago
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Proposes CMRM, a framework for improving classification under label noise without privileged knowledge, using quantile-calibrated regularization.
3d ago
BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis
Combines LLMs with Graph Neural Networks to enhance fMRI brain network analysis by leveraging LLM representations.
3d ago
Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training
Bias-constrained diffusion schedules for PDE emulation with improved reconstruction error and efficient unrolled training.