Ryan P. Adams

TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.

TL;DR: This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

TL;DR: In this article, a deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor, which can generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds.

TL;DR: In this paper, a convolutional neural network that operates directly on graphs is proposed to learn end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.

TL;DR: A method to convert discrete representations of molecules to and from a multidimensional continuous representation that allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds is reported.