One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as ...

Despite the AI hype, ML tools really are proving valuable for leading-edge chip manufacturing. More aggressive feature ...

Serverless computing has shown vast potential for big data analytics applications, especially involving machine learning algorithms. Nevertheless, little consideration has been given in the literature ...

In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely ...

In particular, we first prove its feasibility with an experiment in which three students with essential parallel and distributed machine learning knowledge reproduce different FL algorithms published ...

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data. This includes text from books and websites, images from public databases, ...

EDUCATION Georgia Institute of Technology Sept 2020 - May 2022 M.S. Computer Science Deep Learning ... Mathematics, Algorithms, Databases, Machine Lemming, Artificial Intelligence, Natural Language ...

A current approach uses classical machine learning (CML) algorithms, but they require large datasets, and their performance degrades in small-sample, nonlinear settings. The Australian researchers, ...