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Decomposing reverse-mode automatic differentiation

Decomposing reverse-mode automatic differentiation

20 May 2021
Roy Frostig
Matthew J. Johnson
D. Maclaurin
Adam Paszke
Alexey Radul
ArXiv (abs)PDFHTMLGithub (35484★)

Papers citing "Decomposing reverse-mode automatic differentiation"

4 / 4 papers shown
Fast Finite Width Neural Tangent Kernel
Fast Finite Width Neural Tangent KernelInternational Conference on Machine Learning (ICML), 2022
Roman Novak
Jascha Narain Sohl-Dickstein
S. Schoenholz
AAML
283
72
0
17 Jun 2022
Neural Bregman Divergences for Distance Learning
Neural Bregman Divergences for Distance LearningInternational Conference on Learning Representations (ICLR), 2022
Fred Lu
Edward Raff
Francis Ferraro
187
5
0
09 Jun 2022
Equinox: neural networks in JAX via callable PyTrees and filtered
  transformations
Equinox: neural networks in JAX via callable PyTrees and filtered transformations
Patrick Kidger
Cristian Garcia
377
205
0
30 Oct 2021
Efficient and Modular Implicit Differentiation
Efficient and Modular Implicit DifferentiationNeural Information Processing Systems (NeurIPS), 2021
Mathieu Blondel
Quentin Berthet
Marco Cuturi
Roy Frostig
Stephan Hoyer
Felipe Llinares-López
Fabian Pedregosa
Jean-Philippe Vert
578
317
0
31 May 2021
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