Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

Over the last five years, researchers have started rethinking compression as a computer vision problem, building new solutions with machine learning, particularly using versatile deep learning ...

Faster Video Compression Using Machine Learning. ... When a video is broadcast or streamed, we don't send actual frames but encoded data describing how to synthesise each frame at the end device.

Consequently, it can load datasets up to a few GBs in memory, which means millions, if not billions, of data points. For many machine learning tasks, this is more than enough.

But AI is not only about large data sets, and research in “small data” approaches has grown extensively over the past decade—with so-called transfer learning as an especially promising example.

EPFL researchers have developed a machine learning approach to compressing image data with greater accuracy than learning-free computation methods, with applications for retinal implants and other ...

Machine learning, or ML, is growing in importance for enterprises that want to use their data to improve their customer experience, develop better products and more. But before an enterprise can ...

The Big Data Analytics, Artificial Intelligence and Machine Learning research cluster tackles important problems and develops real-life applications, harnessing technologies to extract insights and ...

Over the last five years, researchers have started rethinking compression as a computer vision problem, building new solutions with machine learning, particularly using versatile deep learning ...

Faster Video Compression Using Machine Learning. ... When a video is broadcast or streamed, we don't send actual frames but encoded data describing how to synthesise each frame at the end device.