A Wagner, A Regev, N Yosef - Nature biotechnology, 2016 - nature.com
Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in …
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
This work leverages recent advances in probabilistic machine learning to discover governing equations expressed by parametric linear operators. Such equations involve, but …
Geometrical deviation is an important factor in determining the quality of a three-dimensional (3D) Surface. For 3D surfaces with complex shapes, the high-definition measurement (HDM) …
DJ Rezende, S Mohamed… - … conference on machine …, 2014 - proceedings.mlr.press
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for …
M Binois, N Wycoff - ACM Transactions on Evolutionary Learning and …, 2022 - dl.acm.org
Bayesian Optimization (BO), the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years. In particular, much …
The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical …
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level …
We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points …