Automatic differentiation in machine learning: a survey

20 February 2015

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

50 / 343 papers shown

Title
Data-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning Li Wang Zhenya Yan 19 80 0 18 Dec 2020
An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data A. Halle L. Campanile A. Hasse AI4CE 16 10 0 11 Dec 2020
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang Ricky T. Q. Chen Christos Tsirigotis Aaron Courville OT 119 95 0 10 Dec 2020
Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations S. Dong Zongwei Li 28 164 0 04 Dec 2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture S. Niaki E. Haghighat Trevor Campbell Xinglong Li R. Vaziri AI4CE 13 203 0 27 Nov 2020
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems Rui Wang Danielle C. Maddix Christos Faloutsos Bernie Wang Rose Yu OOD AI4CE 27 51 0 20 Nov 2020
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss Ross Greer Nachiket Deo Mohan M. Trivedi 35 46 0 12 Nov 2020
Brain-Inspired Learning on Neuromorphic Substrates Friedemann Zenke Emre Neftci 38 87 0 22 Oct 2020
Energy-based error bound of physics-informed neural network solutions in elasticity Mengwu Guo E. Haghighat PINN 51 28 0 18 Oct 2020
Random Coordinate Langevin Monte Carlo Zhiyan Ding Qin Li Jianfeng Lu Stephen J. Wright BDL 16 11 0 03 Oct 2020
Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows Juntao Huang Zhiting Ma Y. Zhou W. Yong AI4CE 43 16 0 28 Sep 2020
BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning Yaohua Liu Risheng Liu 25 10 0 28 Sep 2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Li Li Stephan Hoyer Ryan Pederson Ruoxi Sun E. D. Cubuk Patrick F. Riley K. Burke AI4CE 35 120 0 17 Sep 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 34 79 0 17 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 28 444 0 07 Sep 2020
AIPerf: Automated machine learning as an AI-HPC benchmark Zhixiang Ren Yongheng Liu Tianhui Shi Lei Xie Yue Zhou Jidong Zhai Youhui Zhang Yunquan Zhang Wenguang Chen 27 22 0 17 Aug 2020
Unnormalized Variational Bayes Saeed Saremi BDL 86 1 0 29 Jul 2020
Incremental Without Replacement Sampling in Nonconvex Optimization Edouard Pauwels 38 5 0 15 Jul 2020
On the Iteration Complexity of Hypergradient Computation Riccardo Grazzi Luca Franceschi Massimiliano Pontil Saverio Salzo 48 193 0 29 Jun 2020
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
Approximate Cross-Validation for Structured Models S. Ghosh William T. Stephenson Tin D. Nguyen Sameer K. Deshpande Tamara Broderick 16 15 0 23 Jun 2020
Neural Ordinary Differential Equation Control of Dynamics on Graphs Thomas Asikis Lucas Böttcher Nino Antulov-Fantulin 33 43 0 17 Jun 2020
Dynamic Tensor Rematerialization Marisa Kirisame Steven Lyubomirsky Altan Haan Jennifer Brennan Mike He Jared Roesch Tianqi Chen Zachary Tatlock 27 93 0 17 Jun 2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee Hangyeol Yu Xavier Rival Hongseok Yang 28 40 0 12 Jun 2020
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning Sijia Liu Pin-Yu Chen B. Kailkhura Gaoyuan Zhang A. Hero III P. Varshney 24 224 0 11 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 17 222 0 10 Jun 2020
SALD: Sign Agnostic Learning with Derivatives Matan Atzmon Y. Lipman 24 143 0 09 Jun 2020
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs Beren Millidge Alexander Tschantz Christopher L. Buckley 30 118 0 07 Jun 2020
Deep learning of free boundary and Stefan problems Sizhuang He P. Perdikaris 27 80 0 04 Jun 2020
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs Matilde Gargiani Andrea Zanelli Moritz Diehl Frank Hutter ODL 4 18 0 03 Jun 2020
Transfer learning based multi-fidelity physics informed deep neural network S. Chakraborty PINN OOD AI4CE 12 163 0 19 May 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization Rishikesh Ranade C. Hill Jay Pathak AI4CE 54 123 0 17 May 2020
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks E. Haghighat R. Juanes AI4CE PINN 17 21 0 11 May 2020
Automatic Differentiation in ROOT V. Vassilev A. Efremov O. Shadura PINN 19 5 0 09 Apr 2020
A deep learning framework for solution and discovery in solid mechanics E. Haghighat M. Raissi A. Moure H. Gómez R. Juanes AI4CE PINN 11 56 0 14 Feb 2020
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications Yun Yuan X. Yang Zhao Zhang Shandian Zhe AI4CE 34 95 0 06 Feb 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 35 291 0 13 Jan 2020
Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach H. Hwang Jin Woo Jang Hyeontae Jo Jae Yong Lee 188 36 0 22 Nov 2019
A Simple Differentiable Programming Language M. Abadi G. Plotkin 19 65 0 11 Nov 2019
Physics-Informed Neural Networks for Power Systems George S. Misyris Andreas Venzke Spyros Chatzivasileiadis PINN AI4CE 28 212 0 09 Nov 2019
Deep Learning of Subsurface Flow via Theory-guided Neural Network Nanzhe Wang Dongxiao Zhang Haibin Chang Heng Li AI4CE 30 226 0 24 Oct 2019
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 32 30 0 22 Oct 2019
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences Topi Paananen Michael Riis Andersen Aki Vehtari 27 3 0 17 Oct 2019
Backpropagation in the Simply Typed Lambda-calculus with Linear Negation Aloïs Brunel Damiano Mazza Michele Pagani 22 46 0 27 Sep 2019
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks Ameya Dilip Jagtap Kenji Kawaguchi George Karniadakis ODL 29 85 0 25 Sep 2019
Machine Discovery of Partial Differential Equations from Spatiotemporal Data Ye Yuan Junlin Li Liang Li Frank Jiang Xiuchuan Tang ... J. Gonçalves H. Voss Xiuting Li J. Kurths Han Ding AI4CE 17 9 0 15 Sep 2019
Meta-Learning with Implicit Gradients Aravind Rajeswaran Chelsea Finn Sham Kakade Sergey Levine 24 844 0 10 Sep 2019
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stühmer Richard Turner Sebastian Nowozin DRL BDL CoGe 117 25 0 05 Sep 2019
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data Hao Xu Haibin Chang Dongxiao Zhang AI4CE 22 69 0 13 Aug 2019
Distributed physics informed neural network for data-efficient solution to partial differential equations Vikas Dwivedi N. Parashar Balaji Srinivasan PINN 21 81 0 21 Jul 2019