Machine Learning

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now?

[2603.24143] Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations

Abstract page for arXiv paper 2603.24143: Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations

[2603.24138] Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models

Abstract page for arXiv paper 2603.24138: Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surroga...

[2603.24131] Reservoir-Based Graph Convolutional Networks

Abstract page for arXiv paper 2603.24131: Reservoir-Based Graph Convolutional Networks

[2603.24128] On Gossip Algorithms for Machine Learning with Pairwise Objectives

Abstract page for arXiv paper 2603.24128: On Gossip Algorithms for Machine Learning with Pairwise Objectives

[2603.24126] Likelihood hacking in probabilistic program synthesis

Abstract page for arXiv paper 2603.24126: Likelihood hacking in probabilistic program synthesis

[2603.24113] Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks

Abstract page for arXiv paper 2603.24113: Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks

[2603.24124] The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation

Abstract page for arXiv paper 2603.24124: The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty...

[2603.24105] Causality-Driven Disentangled Representation Learning in Multiplex Graphs

Abstract page for arXiv paper 2603.24105: Causality-Driven Disentangled Representation Learning in Multiplex Graphs

[2603.24093] Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

Abstract page for arXiv paper 2603.24093: Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

[2603.24076] The impact of sensor placement on graph-neural-network-based leakage detection

Abstract page for arXiv paper 2603.24076: The impact of sensor placement on graph-neural-network-based leakage detection

[2603.24044] MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning

Abstract page for arXiv paper 2603.24044: MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning

[2603.24002] Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs

Abstract page for arXiv paper 2603.24002: Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs

[2603.23994] Understanding the Challenges in Iterative Generative Optimization with LLMs

Abstract page for arXiv paper 2603.23994: Understanding the Challenges in Iterative Generative Optimization with LLMs

[2603.23987] Can we generate portable representations for clinical time series data using LLMs?

Abstract page for arXiv paper 2603.23987: Can we generate portable representations for clinical time series data using LLMs?

[2603.23985] Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score

Abstract page for arXiv paper 2603.23985: Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score

[2603.23984] Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing

Abstract page for arXiv paper 2603.23984: Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for ...

[2603.23977] Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception

Abstract page for arXiv paper 2603.23977: Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception

[2603.23961] GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference

Abstract page for arXiv paper 2603.23961: GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference

[2603.23878] The Luna Bound Propagator for Formal Analysis of Neural Networks

Abstract page for arXiv paper 2603.23878: The Luna Bound Propagator for Formal Analysis of Neural Networks

[2603.23871] HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation

Abstract page for arXiv paper 2603.23871: HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation