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Automatic differentiation in machine learning: a survey

Automatic differentiation in machine learning: a survey

20 February 2015
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
    PINN
    AI4CE
    ODL
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Papers citing "Automatic differentiation in machine learning: a survey"

50 / 319 papers shown
Title
Decision-Focused Learning: Foundations, State of the Art, Benchmark and
  Future Opportunities
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi
James Kotary
Senne Berden
Maxime Mulamba
Víctor Bucarey
Tias Guns
Ferdinando Fioretto
AI4CE
28
55
0
25 Jul 2023
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial
  Networks for Stochastic Differential Equations
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
R. Gao
Yufeng Wang
Min Yang
Chuanjun Chen
GAN
34
2
0
21 Jul 2023
Investigation of Compressor Cascade Flow Using Physics- Informed Neural
  Networks with Adaptive Learning Strategy
Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
Zhihui Li
Francesco Montomoli
Sanjiv Sharma
PINN
24
6
0
15 Jul 2023
Stochastic Delay Differential Games: Financial Modeling and Machine
  Learning Algorithms
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms
R. Balkin
Héctor D. Ceniceros
Ruimeng Hu
11
2
0
12 Jul 2023
Addressing Discontinuous Root-Finding for Subsequent Differentiability
  in Machine Learning, Inverse Problems, and Control
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control
Dan Johnson
Ronald Fedkiw
AI4CE
26
2
0
21 Jun 2023
Can Forward Gradient Match Backpropagation?
Can Forward Gradient Match Backpropagation?
Louis Fournier
Stephane Rivaud
Eugene Belilovsky
Michael Eickenberg
Edouard Oyallon
19
16
0
12 Jun 2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood
  Estimation for Latent Gaussian Models
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Alexander Lin
Bahareh Tolooshams
Yves Atchadé
Demba E. Ba
36
1
0
05 Jun 2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained
  Models
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Guillermo Ortiz-Jiménez
Alessandro Favero
P. Frossard
MoMe
51
110
0
22 May 2023
A Survey on Solving and Discovering Differential Equations Using Deep
  Neural Networks
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
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution
  Strategies
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Oscar Li
James Harrison
Jascha Narain Sohl-Dickstein
Virginia Smith
Luke Metz
49
5
0
21 Apr 2023
Scallop: A Language for Neurosymbolic Programming
Scallop: A Language for Neurosymbolic Programming
Ziyang Li
Jiani Huang
Mayur Naik
ReLM
LRM
NAI
21
30
0
10 Apr 2023
A physics-informed neural network framework for modeling
  obstacle-related equations
A physics-informed neural network framework for modeling obstacle-related equations
Hamid EL Bahja
J. C. Hauffen
P. Jung
B. Bah
Issa Karambal
PINN
AI4CE
29
3
0
07 Apr 2023
A differentiable programming framework for spin models
A differentiable programming framework for spin models
T. S. Farias
V. V. Schultz
José C. M. Mombach
Jonas Maziero
24
0
0
04 Apr 2023
Laplace-fPINNs: Laplace-based fractional physics-informed neural
  networks for solving forward and inverse problems of subdiffusion
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion
Xiongbin Yan
Zhi-Qin John Xu
Zheng Ma
32
2
0
03 Apr 2023
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward
  non-intrusive Meta-learning of parametric PDEs
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs
Yanlai Chen
Shawn Koohy
PINN
AI4CE
34
24
0
27 Mar 2023
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy,
  Computational Cost, and Flexibility
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility
Jochen Stiasny
Spyros Chatzivasileiadis
PINN
AI4CE
35
9
0
15 Mar 2023
Improving physics-informed neural networks with meta-learned
  optimization
Improving physics-informed neural networks with meta-learned optimization
Alexander Bihlo
PINN
36
18
0
13 Mar 2023
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed
  Neural Network Training
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
B.-L. Lu
Christian Moya
Guang Lin
PINN
37
11
0
03 Mar 2023
Error convergence and engineering-guided hyperparameter search of PINNs:
  towards optimized I-FENN performance
Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance
Panos Pantidis
Habiba Eldababy
Christopher Miguel Tagle
M. Mobasher
35
20
0
03 Mar 2023
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in
  3D-IC Design
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
Z. Liu
Yixing Li
Jing Hu
Xinling Yu
Shi-En Shiau
Xin Ai
Zhiyu Zeng
Zheng-Wei Zhang
AI4CE
25
16
0
25 Feb 2023
PIFON-EPT: MR-Based Electrical Property Tomography Using
  Physics-Informed Fourier Networks
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
Xinling Yu
José E. C. Serrallés
Ilias I. Giannakopoulos
Z. Liu
Luca Daniel
R. Lattanzi
Zheng-Wei Zhang
24
10
0
23 Feb 2023
h-analysis and data-parallel physics-informed neural networks
h-analysis and data-parallel physics-informed neural networks
Paul Escapil-Inchauspé
G. A. Ruz
PINN
AI4CE
29
2
0
17 Feb 2023
Numerical Methods For PDEs Over Manifolds Using Spectral Physics
  Informed Neural Networks
Numerical Methods For PDEs Over Manifolds Using Spectral Physics Informed Neural Networks
Yuval Zelig
S. Dekel
9
1
0
10 Feb 2023
PINN Training using Biobjective Optimization: The Trade-off between Data
  Loss and Residual Loss
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss
Fabian Heldmann
Sarah Treibert
Matthias Ehrhardt
K. Klamroth
38
20
0
03 Feb 2023
Reliable Prediction Intervals with Directly Optimized Inductive
  Conformal Regression for Deep Learning
Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning
Haocheng Lei
A. Bellotti
26
6
0
02 Feb 2023
Deep neural operators can serve as accurate surrogates for shape
  optimization: A case study for airfoils
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
K. Shukla
Vivek Oommen
Ahmad Peyvan
Michael Penwarden
L. Bravo
A. Ghoshal
Robert M. Kirby
George Karniadakis
33
19
0
02 Feb 2023
On the Correctness of Automatic Differentiation for Neural Networks with
  Machine-Representable Parameters
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Wonyeol Lee
Sejun Park
A. Aiken
PINN
13
6
0
31 Jan 2023
Misspecification-robust Sequential Neural Likelihood for
  Simulation-based Inference
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Ryan P. Kelly
David J. Nott
David T. Frazier
D. Warne
Christopher C. Drovandi
25
10
0
31 Jan 2023
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Tianqi Cui
Tom S. Bertalan
George J. Pappas
M. Morari
Ioannis G. Kevrekidis
Mahyar Fazlyab
AAML
21
2
0
27 Jan 2023
Differentiable bit-rate estimation for neural-based video codec
  enhancement
Differentiable bit-rate estimation for neural-based video codec enhancement
A. Said
M. K. Singh
Reza Pourreza
14
5
0
24 Jan 2023
Federated Automatic Differentiation
Federated Automatic Differentiation
Keith Rush
Zachary B. Charles
Zachary Garrett
FedML
34
1
0
18 Jan 2023
Differentiable modeling to unify machine learning and physical models
  and advance Geosciences
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen
A. Appling
Pierre Gentine
Toshiyuki Bandai
H. Gupta
...
Chris Rackauckas
Tirthankar Roy
Chonggang Xu
Binayak Mohanty
K. Lawson
AI4CE
36
14
0
10 Jan 2023
Valid P-Value for Deep Learning-Driven Salient Region
Valid P-Value for Deep Learning-Driven Salient Region
Daiki Miwa
Vo Nguyen Le Duy
I. Takeuchi
FAtt
AAML
29
14
0
06 Jan 2023
Physics-Informed Neural Networks for Prognostics and Health Management
  of Lithium-Ion Batteries
Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries
Pengfei Wen
Z. Ye
Yong Li
Shaowei Chen
Pu Xie
Shuai Zhao
30
35
0
02 Jan 2023
Efficient and Sound Differentiable Programming in a Functional
  Array-Processing Language
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
Amir Shaikhha
Mathieu Huot
Shabnam Ghasemirad
Andrew Fitzgibbon
S. Jones
Dimitrios Vytiniotis
16
1
0
20 Dec 2022
Physics-informed Neural Networks with Periodic Activation Functions for
  Solute Transport in Heterogeneous Porous Media
Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media
Salah A. Faroughi
Ramin Soltanmohammad
Pingki Datta
S. K. Mahjour
S. Faroughi
18
22
0
17 Dec 2022
Regularized Optimal Transport Layers for Generalized Global Pooling
  Operations
Regularized Optimal Transport Layers for Generalized Global Pooling Operations
Hongteng Xu
Minjie Cheng
36
4
0
13 Dec 2022
PDE-LEARN: Using Deep Learning to Discover Partial Differential
  Equations from Noisy, Limited Data
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data
R. Stephany
Christopher Earls
16
16
0
09 Dec 2022
Fourier Continuation for Exact Derivative Computation in
  Physics-Informed Neural Operators
Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators
Ha Maust
Zong-Yi Li
Yixuan Wang
Daniel Leibovici
O. Bruno
T. Hou
Anima Anandkumar
AI4CE
21
11
0
29 Nov 2022
Neural networks: solving the chemistry of the interstellar medium
Neural networks: solving the chemistry of the interstellar medium
Lorenzo Branca
Andrea Pallottini
14
7
0
28 Nov 2022
Utilising physics-guided deep learning to overcome data scarcity
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai
Laith Alzubaidi
Qingxia Wang
E. Kuhl
Bennamoun
Yuantong T. Gu
PINN
AI4CE
31
3
0
24 Nov 2022
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics
  Informed Neural Nets and strengthens their Approximation Power
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power
Juan Esteban Suarez Cardona
Michael Hecht
24
4
0
23 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in
  Scientific Computing
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
Salah A. Faroughi
N. Pawar
C. Fernandes
Maziar Raissi
Subasish Das
N. Kalantari
S. K. Mahjour
PINN
AI4CE
27
49
0
14 Nov 2022
Astronomia ex machina: a history, primer, and outlook on neural networks
  in astronomy
Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
Michael J. Smith
James E. Geach
35
32
0
07 Nov 2022
A Deep Double Ritz Method (D$^2$RM) for solving Partial Differential
  Equations using Neural Networks
A Deep Double Ritz Method (D2^22RM) for solving Partial Differential Equations using Neural Networks
C. Uriarte
David Pardo
I. Muga
J. Muñoz‐Matute
33
17
0
07 Nov 2022
Physics-informed neural networks for gravity currents reconstruction
  from limited data
Physics-informed neural networks for gravity currents reconstruction from limited data
Mickaël G. Delcey
Y. Cheny
S. Richter
PINN
AI4CE
19
11
0
03 Nov 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey
Partial Differential Equations Meet Deep Neural Networks: A Survey
Shudong Huang
Wentao Feng
Chenwei Tang
Jiancheng Lv
AI4CE
AIMat
24
17
0
27 Oct 2022
SeismicNet: Physics-informed neural networks for seismic wave modeling
  in semi-infinite domain
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Pu Ren
Chengping Rao
Su Chen
Jian-Xun Wang
Hao Sun
Yang Liu
44
41
0
25 Oct 2022
Graphically Structured Diffusion Models
Graphically Structured Diffusion Models
Christian Weilbach
William Harvey
Frank D. Wood
DiffM
35
7
0
20 Oct 2022
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
I. O. Sandoval
Panagiotis Petsagkourakis
Ehecatl Antonio del Rio Chanona
22
9
0
20 Oct 2022
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