Over the last two decades, genome-wide association studies (GWAS) have been deployed at scale to map genotype to phenotype. The success of these studies has driven a broad interest in using polygenic ...

Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth. To address this challenge, we propose an ...

A new technical paper titled “Fast and robust analog in-memory deep neural network training” was published by researchers at IBM Research. “Analog in-memory computing is a promising future technology ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly accessible ...