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Instead of Rewriting Foreign Code for Machine Learning, Automatically
  Synthesize Fast Gradients

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

4 October 2020
William S. Moses
Valentin Churavy
ArXivPDFHTML

Papers citing "Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients"

19 / 19 papers shown
Title
A Common Interface for Automatic Differentiation
A Common Interface for Automatic Differentiation
Guillaume Dalle
Adrian Hill
PINN
VLM
52
0
0
08 May 2025
Value Gradients with Action Adaptive Search Trees in Continuous (PO)MDPs
Value Gradients with Action Adaptive Search Trees in Continuous (PO)MDPs
Idan Lev-Yehudi
Michael Novitsky
Moran Barenboim
Ron Benchetrit
Vadim Indelman
50
0
0
15 Mar 2025
Optimizing Automatic Differentiation with Deep Reinforcement Learning
Optimizing Automatic Differentiation with Deep Reinforcement Learning
Jamie Lohoff
Emre Neftci
61
1
0
28 Jan 2025
JAXbind: Bind any function to JAX
JAXbind: Bind any function to JAX
Jakob Roth
Martin Reinecke
G. Edenhofer
23
1
0
13 Mar 2024
Bidirectional Reactive Programming for Machine Learning
Bidirectional Reactive Programming for Machine Learning
D. Potop-Butucaru
Albert Cohen
Gordon Plotkin
Hugo Pompougnac
KELM
AI4CE
21
0
0
28 Nov 2023
Reverse-Mode AD of Reduce-by-Index and Scan in Futhark
Reverse-Mode AD of Reduce-by-Index and Scan in Futhark
Lotte Maria Bruun
Ulrik Stuhr Larsen
N. Hinnerskov
C. Oancea
13
1
0
05 Oct 2023
A Review of Change of Variable Formulas for Generative Modeling
A Review of Change of Variable Formulas for Generative Modeling
Ullrich Kothe
23
6
0
04 Aug 2023
Understanding Automatic Differentiation Pitfalls
Understanding Automatic Differentiation Pitfalls
Jan Huckelheim
Harshitha Menon
William S. Moses
Bruce Christianson
P. Hovland
Laurent Hascoet
PINN
17
4
0
12 May 2023
Bridging HPC Communities through the Julia Programming Language
Bridging HPC Communities through the Julia Programming Language
Valentin Churavy
William F. Godoy
Carsten Bauer
Hendrik Ranocha
Michael Schlottke-Lakemper
...
Mosè Giordano
E. Schnetter
Samuel Omlin
Jeffrey S. Vetter
Alan Edelman
27
10
0
04 Nov 2022
Differentiable Programming for Earth System Modeling
Differentiable Programming for Earth System Modeling
Maximilian Gelbrecht
Alistair J R White
S. Bathiany
Niklas Boers
21
16
0
29 Aug 2022
Closed-Form Diffeomorphic Transformations for Time Series Alignment
Closed-Form Diffeomorphic Transformations for Time Series Alignment
Iñigo Martinez
E. Viles
Igor García Olaizola
AI4TS
17
7
0
16 Jun 2022
Machines of finite depth: towards a formalization of neural networks
Machines of finite depth: towards a formalization of neural networks
Pietro Vertechi
M. Bergomi
PINN
21
2
0
27 Apr 2022
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader
  Packing and Soft Rasterization
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization
Yusuke Takimoto
Hiroyuki Sato
Hikari Takehara
Keishiro Uragaki
Takehiro Tawara
Xiao Liang
Kentaro Oku
W. Kishimoto
Bo Zheng
32
5
0
04 Apr 2022
New directions for surrogate models and differentiable programming for
  High Energy Physics detector simulation
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann
W. Hopkins
E. Kourlitis
Michael Kagan
Gregor Kasieczka
...
David Shih
Vinicius Mikuni
Benjamin Nachman
K. Pedro
D. Winklehner
27
29
0
15 Mar 2022
AD for an Array Language with Nested Parallelism
AD for an Array Language with Nested Parallelism
Robert Schenck
O. Rønning
Troels Henriksen
C. Oancea
14
10
0
21 Feb 2022
Fitting Matérn Smoothness Parameters Using Automatic Differentiation
Fitting Matérn Smoothness Parameters Using Automatic Differentiation
Christopher J. Geoga
Oana Marin
Michel Schanen
Michael L. Stein
22
13
0
01 Jan 2022
Advances in Neural Rendering
Advances in Neural Rendering
A. Tewari
Justus Thies
B. Mildenhall
P. Srinivasan
E. Tretschk
...
S. Fanello
Jun Zhu
Gordon Wetzstein
Michael Zollhoefer
D. B. Goldman
3DH
AI4CE
53
445
0
10 Nov 2021
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming
  in Julia
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
Frank Schafer
Mohamed Tarek
Lyndon White
Chris Rackauckas
18
5
0
25 Sep 2021
Swift for TensorFlow: A portable, flexible platform for deep learning
Swift for TensorFlow: A portable, flexible platform for deep learning
Brennan Saeta
Denys Shabalin
M. Rasi
Brad Larson
Xihui Wu
...
Saleem Abdulrasool
A. Efremov
Dave Abrahams
Chris Lattner
Richard Wei
HAI
24
11
0
26 Feb 2021
1