When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Revealing the vectors of cellular identity with single-cell genomics
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 …
cells. At the same time, it has reopened definitions of a cell's identity and of the ways in …
[图书][B] Mathematics for machine learning
The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
Machine learning of linear differential equations using Gaussian processes
M Raissi, P Perdikaris, GE Karniadakis - Journal of Computational Physics, 2017 - Elsevier
This work leverages recent advances in probabilistic machine learning to discover
governing equations expressed by parametric linear operators. Such equations involve, but …
governing equations expressed by parametric linear operators. Such equations involve, but …
Geometrical deviation modeling and monitoring of 3D surface based on multi-output Gaussian process
C Zhao, J Lv, S Du - Measurement, 2022 - Elsevier
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) …
(3D) Surface. For 3D surfaces with complex shapes, the high-definition measurement (HDM) …
Stochastic backpropagation and approximate inference in deep generative models
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 …
generalised class of deep, directed generative models, endowed with a new algorithm for …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
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 …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
[HTML][HTML] Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
D Montes de Oca Zapiain, JA Stewart… - npj Computational …, 2021 - nature.com
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 …
evolution of microstructures and associated properties for a wide variety of physical …
[HTML][HTML] ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
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 …
states through molecular characterization of gene expression on the single cell level …
Gaussian processes for big data
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 …
application of Gaussian process (GP) models to data sets containing millions of data points …