Automatic differentiation in machine learning: a survey

20 February 2015

Papers citing "Automatic differentiation in machine learning: a survey"

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Title
Deep learning soliton dynamics and complex potentials recognition for 1D and 2D PT-symmetric saturable nonlinear Schrödinger equations Jin Song Ilya Shenbin 91 25 0 29 Sep 2023
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation Antonio Ferrer-Sánchez Carlos Flores-Garrigós C. Hernani-Morales José J. Orquín-Marqués N. N. Hegade Alejandro Gomez Cadavid Iraitz Montalban Enrique Solano Yolanda Vives-Gilabert J. D. Martín-Guerrero 32 2 0 08 Sep 2023
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics Michael Kagan Lukas Heinrich 32 9 0 31 Aug 2023
Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs Yunuo Cen Zhiwei Zhang Xuanyao Fong 26 1 0 29 Aug 2023
Warped geometric information on the optimisation of Euclidean functions M. Hartmann Bernardo Williams Hanlin Yu Mark Girolami Alessandro Barp Arto Klami 24 2 0 16 Aug 2023
Learning Specialized Activation Functions for Physics-informed Neural Networks Honghui Wang Lu Lu Shiji Song Gao Huang PINN AI4CE 16 12 0 08 Aug 2023
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 57 0 25 Jul 2023
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 Zhihui Li Francesco Montomoli Sanjiv Sharma PINN 32 6 0 15 Jul 2023
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms R. Balkin Héctor D. Ceniceros Ruimeng Hu 16 2 0 12 Jul 2023
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control Dan Johnson Ronald Fedkiw AI4CE 31 2 0 21 Jun 2023
Can Forward Gradient Match Backpropagation? Louis Fournier Stéphane 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 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 Guillermo Ortiz-Jiménez Alessandro Favero P. Frossard MoMe 51 112 0 22 May 2023
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 Oscar Li James Harrison Jascha Narain Sohl-Dickstein Virginia Smith Luke Metz 54 5 0 21 Apr 2023
Scallop: A Language for Neurosymbolic Programming Ziyang Li Jiani Huang Mayur Naik ReLM LRM NAI 24 30 0 10 Apr 2023
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 31 4 0 07 Apr 2023
A differentiable programming framework for spin models T. S. Farias V. V. Schultz José C. M. Mombach Jonas Maziero 35 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 Yanlai Chen Shawn Koohy PINN AI4CE 37 24 0 27 Mar 2023
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility Jochen Stiasny Spyros Chatzivasileiadis PINN AI4CE 38 10 0 15 Mar 2023
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 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 Panos Pantidis Habiba Eldababy Christopher Miguel Tagle M. Mobasher 35 20 0 03 Mar 2023
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach Zhiwei Fang Sizhuang He P. Perdikaris PINN AI4CE 16 5 0 25 Feb 2023
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 30 16 0 25 Feb 2023
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 27 10 0 23 Feb 2023
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 Yuval Zelig S. Dekel 25 1 0 10 Feb 2023
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 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 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 Wonyeol Lee Sejun Park A. Aiken PINN 21 6 0 31 Jan 2023
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 Tianqi Cui Tom S. Bertalan George J. Pappas M. Morari Ioannis G. Kevrekidis Mahyar Fazlyab AAML 27 2 0 27 Jan 2023
Differentiable bit-rate estimation for neural-based video codec enhancement A. Said M. K. Singh Reza Pourreza 22 5 0 24 Jan 2023
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 Chaopeng Shen A. Appling Pierre Gentine Toshiyuki Bandai H. Gupta ... Chris Rackauckas Tirthankar Roy Chonggang Xu Binayak Mohanty K. Lawson AI4CE 42 14 0 10 Jan 2023
Valid P-Value for Deep Learning-Driven Salient Region Daiki Miwa Vo Nguyen Le Duy I. Takeuchi FAtt AAML 32 14 0 06 Jan 2023
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 Amir Shaikhha Mathieu Huot Shabnam Ghasemirad Andrew Fitzgibbon S. Jones Dimitrios Vytiniotis 21 1 0 20 Dec 2022
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 21 22 0 17 Dec 2022
Regularized Optimal Transport Layers for Generalized Global Pooling Operations Hongteng Xu Minjie Cheng 36 4 0 13 Dec 2022
Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation Nicolas Nytko Ali Taghibakhshi Tareq Uz Zaman S. MacLachlan Luke N. Olson Matthew West 18 7 0 10 Dec 2022
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 Ha Maust Zong-Yi Li Yixuan Wang Daniel Leibovici O. Bruno T. Hou Anima Anandkumar AI4CE 29 11 0 29 Nov 2022
Neural networks: solving the chemistry of the interstellar medium Lorenzo Branca Andrea Pallottini 22 7 0 28 Nov 2022
Utilising physics-guided deep learning to overcome data scarcity Jinshuai Bai Laith Alzubaidi Qingxia Wang E. Kuhl Bennamoun Yuantong T. Gu PINN AI4CE 36 3 0 24 Nov 2022
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