27d ago
LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
LNN-PINN physics-informed neural network with liquid residual blocks for improved predictive accuracy on complex problems.
27d ago
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
xRFM feature learning models for tabular data providing accurate, scalable, and interpretable alternatives to gradient boosted trees.
27d ago
Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Simulation-based inference methods for generating synthetic datasets to evaluate causal estimators with varying treatment effects.
27d ago
A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations
Data-driven interpolation method on smooth manifolds using diffusion processes and Voronoi tessellations without training.
27d ago
LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios
LoFT parameter-efficient fine-tuning approach for long-tailed semi-supervised learning leveraging foundation models.
27d ago
ACT: Agentic Classification Tree
Agentic Classification Tree (ACT) combining LLMs with decision trees for transparent, interpretable decisions on unstructured data.
27d ago
Fewer Weights, More Problems: A Practical Attack on LLM Pruning
Security analysis exposing vulnerabilities in LLM weight pruning methods used by inference engines like vLLM.
27d ago
A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies
Theoretical finite-time analysis of Q-learning with time-varying policies under minimal assumptions for Markov decision processes.
27d ago
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Self-evolving Post-Training (SePT) method enabling LLMs to improve reasoning without external rewards through self-generated training data.
27d ago
Deep Gaussian Processes for Functional Maps
Deep Gaussian Processes for learning mappings between functional spaces with uncertainty quantification for spatiotemporal forecasting and climate modeling.
27d ago
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
Information-theoretic analysis of out-of-distribution generalization in meta-reinforcement learning with bounds under distribution shift scenarios.
27d ago
SPORE: Skeleton Propagation Over Recalibrating Expansions
Classical clustering algorithm handling arbitrary cluster geometry without global density assumptions using skeleton propagation and recalibrating expansions.
27d ago
SynQuE: Estimating Synthetic Dataset Quality Without Annotations
Method for ranking synthetic datasets by real-world performance without annotations, establishing benchmarks for synthetic data quality estimation.
27d ago
FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis
Fairness-aware stroke diagnosis framework combining domain-adversarial training with group distributionally robust optimization for healthcare equity.
27d ago
Controllable protein design with particle-based Feynman-Kac steering
Feynman-Kac framework for guiding diffusion-based generative models toward proteins with specified properties and tailored structures.
27d ago
A Unified Stability Analysis of SAM vs SGD: Role of Data Coherence and Emergence of Simplicity Bias
Unified stability analysis comparing SAM and SGD optimization algorithms showing role of data coherence and simplicity bias in generalization.
27d ago
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
Comparative analysis of transformer models (DistilBERT) versus psycholinguistic features for detecting business email compromise attacks.
27d ago
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Framework addressing structural overfitting in graph neural networks for missing feature imputation using distribution-aware rectification.
27d ago
Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy
Federated learning approach for vehicle edge caching using personalized distillation to predict user content preferences while preserving privacy.
27d ago
Random-Bridges as Stochastic Transports for Generative Models
Random-bridges framework for generative models using stochastic processes conditioned on target distributions for flexible transport between distributions.
27d ago
GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
Electric load forecasting model integrating multi-source textual data (news, social media, policies) with temporal grid-aware predictions.
27d ago
Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics
Mamba-based neural operator framework for accurate chemical kinetics modeling in combustion simulations using efficient temporal modeling.
27d ago
SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
Distribution restoration method using noisy samples and optimal transport to recover fully observed data from partial corrupted observations.
27d ago
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
Method using large language models to measure semantic similarity in categorical data clustering by bridging gap in attribute distance representation.
27d ago
ASSS: A Differentiable Adversarial Framework for Task-Aware Data Reduction
Differentiable adversarial framework for task-aware data reduction using learnable selector and minimax optimization to identify informative samples.
27d ago
From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
LLM-based hardware-aware quantization agent automating model quantization for efficient LLM deployment on resource-constrained hardware.
27d ago
Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Theoretical analysis of implicit bias in stochastic learning using geometric perspective to explain solution selection in overparameterized models.
27d ago
HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
Split learning optimization method reducing memory overhead for LLM training on edge devices using hybrid-order optimization instead of first-order approaches.
27d ago
Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on Invertible Compression and Learnable Prediction
Neural memory storage architecture for LLMs with invertible compression and learnable prediction for runtime memory.
27d ago
InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
Information-theoretic approach for designing shared visual tokenizers in unified multimodal LLMs.
27d ago
RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models
Safety alignment framework addressing unique challenges of sparse routing in Mixture-of-Experts language models.
27d ago
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
Training framework for multimodal systems to maintain performance when input channels are lost at deployment.
27d ago
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
Pruning framework for physics-informed neural networks to improve robustness to noise in PDE inverse problems.
27d ago
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Time series imputation method using channel-head binding for handling diverse missing patterns.
27d ago
MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
Training method for LLMs to directly model generative reasoning process in scientific discovery applications.
27d ago
KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
Deep learning framework for diagnosing obstructive sleep apnea from oximetry data with clinical knowledge integration.
27d ago
NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning algorithm for general-sum games with convergence guarantees in heterogeneous agent settings.
27d ago
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
Optimization algorithm extending exponential moving average with adaptive rates and zero-noise optimality guarantees.
27d ago
A Grammar of Machine Learning Workflows
Formal grammar framework preventing data leakage in ML workflows through structural constraints and assessment gates.
27d ago
Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
Reinforcement learning method for post-training reasoning models using hindsight feedback in sparse reward environments.
27d ago
Security Considerations for Artificial Intelligence Agents
Security considerations and recommendations for AI agents from Perplexity based on operating agentic systems in production environments.
27d ago
Brittlebench: Quantifying LLM robustness via prompt sensitivity
Framework for measuring LLM robustness to prompt variations, typos, and alternative phrasings in real-world inputs.
27d ago
AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
Predictive maintenance framework for connected vehicles integrating sensor and environmental data with ML models.
27d ago
Accelerating Byzantine-Robust Distributed Learning with Compressed Communication via Double Momentum and Variance Reduction
Distributed learning algorithm combining Byzantine robustness with communication compression for collaborative ML systems.
27d ago
Path-Constrained Mixture-of-Experts
Research on sparse Mixture-of-Experts architectures proposing expert path perspective to understand token routing patterns across layers.
27d ago
Improving RCT-Based CATE Estimation Under Covariate Mismatch via Calibrated Alignment
CALM: method for heterogeneous treatment effect estimation combining RCT and observational study data with covariate mismatch.
27d ago
How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models
Theoretical analysis of pattern formation in diffusion models explained via out-of-equilibrium phase transitions.
27d ago
Learning Sampled-data Control for Swarms via MeanFlow
MeanFlow-based learning approach for controlling large-scale swarms with limited sampled-data updates.
27d ago
LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models
LLM-ODE: uses LLMs to discover governing equations of dynamical systems from data, improving on genetic programming approaches.
27d ago
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
ALMAB-DC: sequential experimental design framework combining active learning, multi-armed bandits and distributed computing for black-box optimization.