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Machine learning in context of physical systems merits a re-examination of the learning strategy.
NeuroAnimator: fast neural network emulation and control of physics-based models
Radek Grzeszczuk, Demetri Terzopoulos, and Geoffrey Hinton · 2000
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Approximation with artificial neural networks
Balázs Csanád Csáji · 2001
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Computing the physical parameters of rigid-body motion from video
Kiran S Bhat, Steven M Seitz, Jovan Popović, and Pradeep K Khosla · 2002
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging
Andreas Velten, Thomas Willwacher, Otkrist Gupta, Ashok Veeraraghavan, Moungi G Bawendi, and Ramesh Raskar · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2015
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Multimodal deep learning for robust rgb-d object recognition
Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin Riedmiller, and Wolfram Burgard · 2015
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
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Learning sensor multiplexing design through back-propagation
Ayan Chakrabarti · 2016
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Computational imaging for vlbi image reconstruction
Katherine L Bouman, Michael D Johnson, Daniel Zoran, Vincent L Fish, Sheperd S Doeleman, and William T Freeman · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Lensless computational imaging through deep learning
Ayan Sinha, Justin Lee, Shuai Li, and George Barbastathis · 2017
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Deep convolutional neural network for inverse problems in imaging
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, and Michael Unser · 2017
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A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction
Eunhee Kang, Junhong Min, and Jong Chul Ye · 2017
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Physics-guided neural networks (pgnn): An application in lake temperature modeling
Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar · 2017
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Unrolled optimization with deep priors
Steven Diamond, Vincent Sitzmann, Felix Heide, and Gordon Wetzstein · 2017
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Pde-net: Learning pdes from data
Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
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Pointfusion: Deep sensor fusion for 3d bounding box estimation
Danfei Xu, Dragomir Anguelov, and Ashesh Jain · 2018
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Deep learning in holography and coherent imaging
Yair Rivenson, Yichen Wu, and Aydogan Ozcan · 2019
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Physics-based neural networks for shape from polarization
Yunhao Ba, Rui Chen, Yiqin Wang, Lei Yan, Boxin Shi, and Achuta Kadambi · 2019
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Block coordinate regularization by denoising
Yu Sun, Jiaming Liu, and Ulugbek S Kamilov · 2019
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Deep-storm: super-resolution single-molecule microscopy by deep learning
Elias Nehme, Lucien E Weiss, Tomer Michaeli, and Yoav Shechtman · 2018
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Deep learning approach for fourier ptychography microscopy
Thanh Nguyen, Yujia Xue, Yunzhe Li, Lei Tian, and George Nehmetallah · 2018
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Low photon count phase retrieval using deep learning
Alexandre Goy, Kwabena Arthur, Shuai Li, and George Barbastathis · 2018
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End-to-end differentiable physics for learning and control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, and J Zico Kolter · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Proxylessnas: Direct neural architecture search on target task and hardware
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Deep hidden physics models: Deep learning of nonlinear partial differential equations
Maziar Raissi · 2018
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Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2019
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Combining physical simulators and object-based networks for control
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Geo-supervised visual depth prediction
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Physics-based learned design: Optimized coded-illumination for quantitative phase imaging
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Learned reconstructions for practical mask-based lensless imaging
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Learning to separate multiple illuminants in a single image
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Heavy rain image restoration: Integrating physics model and conditional adversarial learning
Ruoteng Li, Loong-Fah Cheong, and Robby T Tan · 2019
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On the use of deep learning for computational imaging
George Barbastathis, Aydogan Ozcan, and Guohai Situ · 2019
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First m87 event horizon telescope results. iv. imaging the central supermassive black hole
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Darts+: Improved differentiable architecture search with early stopping
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