Machine learning

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.

By giving algorithms the ability to generalize, researchers are expanding the range of problems that can be tackled with artificial intelligence.

A machine learning method that finds defects or dimensional deviation on 3D-printed surfaces ‘on-the-fly’ is paving the way for smart, fully automated systems.

A hybrid approach combining deep learning with Bayesian inference has enabled more accurate, efficient and automatic crack detection.

By breaking complex actions into their basic components, researchers have developed a versatile framework that enables robots to learn from human demonstrators.

Leveraging both artificial intelligence and field knowledge may prove to be a superior strategy for predicting machine health.

A large genetic study has identified distinct features of lung cancer in East Asians, which which could improve prediction and treatment outcomes.

Researchers have created a new computational framework that is set to transform the online shopping experience.

A new deep learning method increases the accuracy and range of applications for computer vision platforms.