Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Nair Prasanth B"'
Autor:
Course, Kevin, Nair, Prasanth B.
We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length of the time series, and the stiffness of the approximate differential
Externí odkaz:
http://arxiv.org/abs/2312.10550
Computational modeling of high entropy alloys (HEA) is challenging given the scalability issues of Density functional theory (DFT) and the non-availability of Interatomic potentials (IP) for molecular dynamics simulations (MD). This work presents a c
Externí odkaz:
http://arxiv.org/abs/2302.06844
Publikováno v:
Advances in Neural Information Processing Systems. Vol. 33 (2020), pp. 18716-18726
We present a method for learning generalized Hamiltonian decompositions of ordinary differential equations given a set of noisy time series measurements. Our method simultaneously learns a continuous time model and a scalar energy function for a gene
Externí odkaz:
http://arxiv.org/abs/2104.05096
Autor:
Evans, Trefor W., Nair, Prasanth B.
We introduce a stochastic variational inference procedure for training scalable Gaussian process (GP) models whose per-iteration complexity is independent of both the number of training points, $n$, and the number basis functions used in the kernel a
Externí odkaz:
http://arxiv.org/abs/2006.03015
Publikováno v:
BioMedical Engineering OnLine, Vol 6, Iss 1, p 47 (2007)
Abstract Background: Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric
Externí odkaz:
https://doaj.org/article/0c1a0b6e9bea4758b3f1280cc7adec4e
Autor:
Evans, Trefor W., Nair, Prasanth B.
We explore a new research direction in Bayesian variational inference with discrete latent variable priors where we exploit Kronecker matrix algebra for efficient and exact computations of the evidence lower bound (ELBO). The proposed "DIRECT" approa
Externí odkaz:
http://arxiv.org/abs/1809.04279
Autor:
Evans, Trefor W., Nair, Prasanth B.
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses. The first method ignores the gaps using spars
Externí odkaz:
http://arxiv.org/abs/1808.03351
Autor:
Evans, Trefor W., Nair, Prasanth B.
We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in $\mathcal{O}(p)$ time. Our GRIEF kernel consists of $p$ eigenfunctions found using a Nystrom a
Externí odkaz:
http://arxiv.org/abs/1807.02125
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 15 May 2022 395
Publikováno v:
In Computational Materials Science August 2019 166:30-41