Jamie Smith
Jamie Smith
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Cited by
Cited by
Machine learning–accelerated computational fluid dynamics
D Kochkov, JA Smith, A Alieva, Q Wang, MP Brenner, S Hoyer
Proceedings of the National Academy of Sciences 118 (21), e2101784118, 2021
Learning memory access patterns
M Hashemi, K Swersky, J Smith, G Ayers, H Litz, J Chang, C Kozyrakis, ...
International Conference on Machine Learning, 1919-1928, 2018
TF-Agents: A library for reinforcement learning in tensorflow
S Guadarrama, A Korattikara, O Ramirez, P Castro, E Holly, S Fishman, ...
GitHub repository, 2018
Number-theoretic nature of communication in quantum spin systems
C Godsil, S Kirkland, S Severini, J Smith
Physical review letters 109 (5), 050502, 2012
Strongly cospectral vertices
C Godsil, J Smith
arXiv preprint arXiv:1709.07975, 2017
Score-based diffusion models as principled priors for inverse imaging
BT Feng, J Smith, M Rubinstein, H Chang, KL Bouman, WT Freeman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
Estimating the spectral density of large implicit matrices
RP Adams, J Pennington, MJ Johnson, J Smith, Y Ovadia, B Patton, ...
arXiv preprint arXiv:1802.03451, 2018
Learning to correct spectral methods for simulating turbulent flows
G Dresdner, D Kochkov, P Norgaard, L Zepeda-Núñez, JA Smith, ...
arXiv preprint arXiv:2207.00556, 2022
Tensorflow estimators: Managing simplicity vs. flexibility in high-level machine learning frameworks
HT Cheng, Z Haque, L Hong, M Ispir, C Mewald, I Polosukhin, ...
Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017
Neural general circulation models
D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ...
arXiv preprint arXiv:2311.07222, 2023
Variational data assimilation with a learned inverse observation operator
T Frerix, D Kochkov, J Smith, D Cremers, M Brenner, S Hoyer
International Conference on Machine Learning, 3449-3458, 2021
Algorithms for quantum computers
J Smith, M Mosca
arXiv preprint arXiv:1001.0767, 2010
TF-Agents: A library for reinforcement learning in tensorflow, 2018
S Guadarrama, A Korattikara, O Ramirez, P Castro, E Holly, S Fishman, ...
URL https://github. com/tensorflow/agents, 2019
Optimal control of nonequilibrium systems through automatic differentiation
MC Engel, JA Smith, MP Brenner
Physical Review X 13 (4), 041032, 2023
Deep learning for Bayesian optimization of scientific problems with high-dimensional structure
S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljačić
arXiv preprint arXiv:2104.11667, 2021
Scalable and flexible deep Bayesian optimization with auxiliary information for scientific problems
S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljacic
arXiv preprint arXiv:2104.11667 1 (2), 3, 2021
Critiquing protein family classification models using sufficient input subsets
B Carter, M Bileschi, J Smith, T Sanderson, D Bryant, D Belanger, ...
Journal of Computational Biology 27 (8), 1219-1231, 2020
Algebraic aspects of multi-particle quantum walks
J Smith
University of Waterloo, 2012
Ensembles of classifiers: a bias-variance perspective
N Gupta, J Smith, B Adlam, ZE Mariet
Transactions on Machine Learning Research, 2022
Strongly cospectral vertices, 2017
C Godsil, J Smith
arXiv preprint arxiv:1709.07975, 0
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