Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

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 …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that
accurately detect COVID-19 in chest radiographs. However, the robustness of these systems …

Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

Modern practice for training classification deepnets involves a terminal phase of training
(TPT), which begins at the epoch where training error first vanishes. During TPT, the training …

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities
to understand tissue organization and function in spatial context. However, it is still …

We demonstrate unprecedented accuracy for rapid gravitational wave parameter estimation
with deep learning. Using neural networks as surrogates for Bayesian posterior distributions …

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes.
However, for most surgical procedures, the details of intraoperative surgical actions, which …

The development of enhanced sampling methods has greatly extended the scope of
atomistic simulations, allowing long-time phenomena to be studied with accessible …