Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Psychology Today

Diagnosing Schizophrenia With Machine Learning

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average ...
IEEE

Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...
IEEE

An Efficient Checkpointing System for Large Machine Learning Model Training

Abstract: Checkpointing is one of the fundamental techniques to resume training while system fails. It has been generally used in various domains, such as high-performance computing (HPC) and machine ...
CoinDesk

How AI is helping retail traders exploit prediction market 'glitches' to make easy money

A fully automated bot quietly captured micro-arbitrage opportunities on short-term crypto prediction markets, netting nearly ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...