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

The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

The Poisson distribution is widely used in artificial intelligence (AI) and machine learning. In Bayesian inference, probability distributions often help solve problems that would otherwise be ...

Explore how probability distribution functions are integral in training and evaluating machine learning models for accurate predictions and insights.

Probability density function (PDF) estimation is a constantly important topic in the fields related to artificial intelligence and machine learning. This paper is dedicated to considering problems on ...

Abstract: We present a theoretical framework of probabilistic learning derived from the maximum probability (MP) theorem shown in this article. In this probabilistic framework, a model is defined as ...

Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and ...

Water molecules at the ligand–protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning ...