Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

Abstract: In recent years, network representation learning (NRL) has attracted increasing attention due to its efficiency and effectiveness to analyze network structural data. NRL aims to learn ...

The project for which Hulsebos received the grant is called DataLibra, which runs from 2024 to 2029. Over those five years, ...

AI is transforming law practice, but ethical use is essential. Lawyers must ensure transparency, accountability, and follow ...

de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

Researchers from Imperial and its spinout company SOLVE Chemistry have presented a chemical dataset at the prestigious AI conference NeurIPS that could help accelerate the use of machine learning to ...

Cryptography secures communication in banking, messaging, and blockchain. Good algorithms (AES, RSA, ECC, SHA-2/3, ChaCha20) are secure, efficient, and widely trusted. Bad algorithms (DES, MD5, SHA-1, ...

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...

Decentralized finance (DeFi) is a revolutionary shift in the financial landscape, offering a blockchain-based system that facilitates transactions without relying on traditional intermediaries like ...

Artificial intelligence models can secretly transmit dangerous inclinations to one another like a contagion, a recent study found. Experiments showed that an AI model that’s training other models can ...