Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
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 …
Neural operator: Learning maps between function spaces with applications to pdes
The classical development of neural networks has primarily focused on learning map**s
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …
Accurate global machine learning force fields for molecules with hundreds of atoms
Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Despite advances in scalable models, the inference tools used for Gaussian processes
(GPs) have yet to fully capitalize on developments in computing hardware. We present an …
(GPs) have yet to fully capitalize on developments in computing hardware. We present an …
Exact Gaussian processes on a million data points
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows
with the available data. However, computational constraints with standard inference …
with the available data. However, computational constraints with standard inference …
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …
so far could hardly be used in large scale problems, since naïve implementations scale …
Hilbert space methods for reduced-rank Gaussian process regression
This paper proposes a novel scheme for reduced-rank Gaussian process regression. The
method is based on an approximate series expansion of the covariance function in terms of …
method is based on an approximate series expansion of the covariance function in terms of …
Cola: Exploiting compositional structure for automatic and efficient numerical linear algebra
Many areas of machine learning and science involve large linear algebra problems, such as
eigendecompositions, solving linear systems, computing matrix exponentials, and trace …
eigendecompositions, solving linear systems, computing matrix exponentials, and trace …
A survey of machine learning techniques in structural and multidisciplinary optimization
Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …
applications in the field of structural and multidisciplinary optimization over the last few …