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1811.05031
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A Review of automatic differentiation and its efficient implementation
12 November 2018
C. Margossian
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Papers citing
"A Review of automatic differentiation and its efficient implementation"
26 / 26 papers shown
Title
A Common Interface for Automatic Differentiation
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Coherence-based Approximate Derivatives via Web of Affine Spaces Optimization
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24
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26 Apr 2025
Optimizing Automatic Differentiation with Deep Reinforcement Learning
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Emre Neftci
61
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Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization
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Claudius Kienle
Darko Katic
Rainer Jäkel
Michael Beetz
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posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson
Jakob Torgander
Paul-Christian Bürkner
Lu Zhang
Bob Carpenter
Aki Vehtari
42
6
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06 Jul 2024
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems
A. Thiruthummal
Sergiy Shelyag
Eun-Jin Kim
28
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07 Jun 2024
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
Matthias Lehmann
40
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24 Jan 2024
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks
Hyeonjung Jung
Jung
Jayant Gupta
B. Jayaprakash
Matthew J. Eagon
Harish Selvam
Carl Molnar
W. Northrop
Shashi Shekhar
AI4CE
35
5
0
26 Apr 2023
Physics informed WNO
N. N.
Tapas Tripura
S. Chakraborty
30
28
0
12 Feb 2023
A Fully First-Order Method for Stochastic Bilevel Optimization
Jeongyeol Kwon
Dohyun Kwon
S. Wright
Robert D. Nowak
30
68
0
26 Jan 2023
A Deep Double Ritz Method (D
2
^2
2
RM) for solving Partial Differential Equations using Neural Networks
C. Uriarte
David Pardo
I. Muga
J. Muñoz‐Matute
36
18
0
07 Nov 2022
Automatic differentiation and the optimization of differential equation models in biology
S. Frank
22
6
0
10 Jul 2022
Differentiable solver for time-dependent deformation problems with contact
Zizhou Huang
Davi C. Tozoni
Arvi Gjoka
Z. Ferguson
T. Schneider
Daniele Panozzo
Denis Zorin
AI4CE
35
18
0
26 May 2022
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Juho Timonen
Nikolas Siccha
Benjamin B. Bales
Harri Lähdesmäki
Aki Vehtari
22
3
0
18 May 2022
Optimizing differential equations to fit data and predict outcomes
S. Frank
33
4
0
16 Apr 2022
Adjoint-aided inference of Gaussian process driven differential equations
Paterne Gahungu
Christopher W. Lanyon
Mauricio A. Alvarez
Engineer Bainomugisha
M. Smith
Richard D. Wilkinson
26
5
0
09 Feb 2022
Efficient Automatic Differentiation of Implicit Functions
C. Margossian
M. Betancourt
27
2
0
28 Dec 2021
Gradients are Not All You Need
Luke Metz
C. Freeman
S. Schoenholz
Tal Kachman
28
93
0
10 Nov 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Jeremy Yu
Lu Lu
Xuhui Meng
George Karniadakis
PINN
AI4CE
29
451
0
01 Nov 2021
Coarsening Optimization for Differentiable Programming
Xipeng Shen
Guoqiang Zhang
Irene Dea
S. Andow
Emilio Arroyo-Fang
...
E. Meijer
Steffi Stumpos
Alanna Tempest
Christy Warden
Shannon Yang
28
2
0
05 Oct 2021
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
Eric Heiden
Miles Macklin
Yashraj S. Narang
D. Fox
Animesh Garg
Fabio Ramos
20
92
0
25 May 2021
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele
G. Fissore
Adrián Javaloy
Bernhard Schölkopf
Aapo Hyvarinen
DRL
19
22
0
26 Jun 2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee
Hangyeol Yu
Xavier Rival
Hongseok Yang
17
40
0
12 Jun 2020
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINN
AI4CE
25
1,485
0
10 Jul 2019
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,266
0
09 Jun 2012
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