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
Algorithms for quantum computers
J Smith, M Mosca
arXiv preprint arXiv:1001.0767, 2010
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
Strongly cospectral vertices
C Godsil, J Smith
arXiv preprint arXiv:1709.07975, 2017
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
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, 0
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
k-Boson quantum walks do not distinguish arbitrary graphs
J Smith
arXiv preprint arXiv:1004.0206, 2010
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
On the limitations of graph invariants inspired by quantum walks
J Smith
Electronic Notes in Discrete Mathematics 38, 795-801, 2011
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
C Xia, C Mewald, D Sculley, D Soergel, G Roumpos, HT Cheng, ...
Optimal control of nonequilibrium systems through automatic differentiation
MC Engel, JA Smith, MP Brenner
arXiv preprint arXiv:2201.00098, 2022
Machine learning accelerated computational fluid dynamics
A Alieva, D Kochkov, JA Smith, M Brenner, Q Wang, S Hoyer
Optimal batch variance with second-order marginals
Z Mariet, J Robinson, J Smith, S Sra, S Jegelka
ICML Workshop 2, 2020
Cellular algebras and graph invariants based on quantum walks
J Smith
arXiv preprint arXiv:1103.0262, 2011
Understanding the bias-variance tradeoff of Bregman divergences
B Adlam, N Gupta, Z Mariet, J Smith
arXiv preprint arXiv:2202.04167, 2022
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