Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

Cyber threats are increasing in speed and complexity, driving the need for advanced detection techniques. Machine learning is ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Effect of KROS 101, a small molecule GITR ligand agonist, on T effector cells, T reg cells and intratumoral CD8 T cell cytotoxicity. Phase 1 study of DK210 (EGFR), a tumor-targeted IL2 x IL10 dual ...

New AGI lab aims to revolutionize machine learning with symbolic models, moving beyond traditional deep learning.

A machine learning model using routine clinical data more accurately predicted 5-year heart failure risk in patients with CKD ...

This study develops an ML-assisted approach for accelerating the discovery of HER electrocatalysts in multi-principal element ...

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential ...