Machine Learning

‘The cost of compute is far beyond the costs of the employees’: Nvidia exec says right now AI is more expensive than paying human workers

Nvidia’s vice president of applied deep learning, Bryan Catanzaro, recently stated that for his team, “the cost of compute is far beyond ...

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...

Improving AI models’ ability to explain their predictions

[2604.05164] Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

Abstract page for arXiv paper 2604.05164: Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

[2604.05134] Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning

Abstract page for arXiv paper 2604.05134: Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement ...

[2604.05112] Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner

Abstract page for arXiv paper 2604.05112: Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner

[2604.05077] Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing

Abstract page for arXiv paper 2604.05077: Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representatio...

[2604.05072] Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling

Abstract page for arXiv paper 2604.05072: Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Mo...

[2604.05064] Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

Abstract page for arXiv paper 2604.05064: Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

[2604.05057] Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems

Abstract page for arXiv paper 2604.05057: Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Le...

[2604.05045] PCA-Driven Adaptive Sensor Triage for Edge AI Inference

Abstract page for arXiv paper 2604.05045: PCA-Driven Adaptive Sensor Triage for Edge AI Inference

[2604.05042] Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

Abstract page for arXiv paper 2604.05042: Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

[2604.04999] PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities

Abstract page for arXiv paper 2604.04999: PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities

[2604.04998] El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

Abstract page for arXiv paper 2604.04998: El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

[2604.04996] Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

Abstract page for arXiv paper 2604.04996: Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

[2604.04988] Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

Abstract page for arXiv paper 2604.04988: Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

[2604.04987] Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling

Abstract page for arXiv paper 2604.04987: Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling

[2604.04986] Enhancing sample efficiency in reinforcement-learning-based flow control: replacing the critic with an adaptive reduced-order model

Abstract page for arXiv paper 2604.04986: Enhancing sample efficiency in reinforcement-learning-based flow control: replacing the critic ...

[2604.04983] Territory Paint Wars: Diagnosing and Mitigating Failure Modes in Competitive Multi-Agent PPO

Abstract page for arXiv paper 2604.04983: Territory Paint Wars: Diagnosing and Mitigating Failure Modes in Competitive Multi-Agent PPO

[2604.04971] A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks

Abstract page for arXiv paper 2604.04971: A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks

Anthropic's latest AI model identifies 'thousands of zero-day vulnerabilities' in 'every major operating system and every major web browser' — Claude Mythos Preview sparks race to fix critical bugs, some unpatched for decades

Thinking small: How small language models could lessen the AI energy burden

Anthropic says its most powerful AI cyber model is too dangerous to release publicly — so it built Project Glasswing