Machine learning

Researchers have built a navigation algorithm that leverages multidimensional real-world data to map the fastest and most efficient route.

To fully realise the potential of medical technology, A*STAR scholar Daryl Jude Lawrence explains why scientists should move beyond the lab and experience the inner workings of industry-level production and entrepreneurship.

A*STAR computer scientists develop a compression method to shrink energy and hardware requirements for complex computational platforms on everyday devices.

A purpose-built machine learning platform takes the guesswork out of catalyst formulations for green energy generation.

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.