Machine learning

Computer scientists develop an algorithm that tracks moving objects faster and more accurately than current platforms.

A new neural network designed by A*STAR researchers can train algorithms to visually detect defective products in production lines with limited data.

A new machine learning model processes datasets from different sources simultaneously, paving the way for major advancements in medicine and beyond.

Materials science and computational techniques come together to advance new design schemes for aerospace alloys.

A new data selector helps more accurately train machine learning algorithms by recycling datasets from other applications.

A computational platform designed by A*STAR researchers accurately predicts a machine’s functional lifespan even with limited data.

Machine learning and computational platforms are helping develop robust protocols for manufacturing an emerging class of strong, super lightweight alloys.

A novel machine learning framework makes it faster and easier to train robots that can perform tasks with unprecedented precision.

A*STAR researchers have created the first machine learning model for predicting clinical outcomes of stem cell therapies in patients with cartilage disorders.

Machine learning and artificial intelligence have the power to transform chemical research, says A*STAR National Science Scholar Jacqueline Tan.

From advanced materials to atomic super sensors, A*STAR scientists are harnessing the power of quantum to the fullest, ushering in the next generation of trailblazing technologies.

Deep learning algorithms for predicting machine failures in industrial settings can be compressed without compromising their performance, say A*STAR researchers.