Machine learning

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.

A new machine learning technique can predict the dynamics between microbes and therapeutics with unprecedented accuracy.

A*STAR researchers use digital drug simulators to predict the efficacies of repurposed antivirals for treating COVID-19.

Researchers are simulating real-world complexity in machine learning models to ensure their safety before they are deployed in the wild.

A*STAR scientists can now classify immune cell populations with greater precision thanks to a machine-learning algorithm.