The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

Efficient rendering of photo‐realistic virtual worlds is a long standing effort of computer
graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic …

Animal genomes are folded into loops and topologically associating domains (TADs) by
CTCF and loop-extruding cohesins, but the live dynamics of loop formation and stability …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

RNA molecules adopt three-dimensional structures that are critical to their function and of
interest in drug discovery. Few RNA structures are known, however, and predicting them …

We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …

Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …

Deep neural networks excel at finding hierarchical representations that solve complex tasks
over large datasets. How can we humans understand these learned representations? In this …

Using the Deep Potential methodology, we construct a model that reproduces accurately the
potential energy surface of the SCAN approximation of density functional theory for water …