Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

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

We combine amortized neural posterior estimation with importance sampling for fast and
accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian …

We present results of an all-sky search for continuous gravitational waves which can be
produced by spinning neutron stars with an asymmetry around their rotation axis, using data …

We introduce the use of autoregressive normalizing flows for rapid likelihood-free inference
of binary black hole system parameters from gravitational-wave data with deep neural …

We present a novel method for sampling iso-likelihood contours in nested sampling using a
type of machine learning algorithm known as normalizing flows and incorporate it into our …

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data
Challenge. For this challenge, participating groups had to identify gravitational-wave signals …

The current and upcoming generations of gravitational wave experiments represent an
exciting step forward in terms of detector sensitivity and performance. For example, key …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …