Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, possess ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

A Chinese quantitative trading fund has submitted a research paper to one of the world’s top artificial intelligence (AI) conferences, detailing a new training technique that it said could outperform ...

Federated Learning (FL) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional ...

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A Tokyo Tech ...

This repo contains the JAX implementation of our ICLR 2024 paper, Neural Spectral Methods: Self-supervised learning in the spectral domain. Yiheng Du, Nithin ...