@conference {rizzuti2019EAGElis, title = {Learned iterative solvers for the Helmholtz equation}, booktitle = {EAGE Annual Conference Proceedings}, year = {2019}, note = {(EAGE, Copenhagen)}, month = {06}, abstract = {We propose a {\textquoteleft}learned{\textquoteright} iterative solver for the Helmholtz equation, by combining traditional Krylov-based solvers with machine learning. The method is, in principle, able to circumvent the shortcomings of classical iterative solvers, and has clear advantages over purely data-driven ap- proaches. We demonstrate the effectiveness of this approach under a 1.5-D assumption, when ade- quate a priori information about the velocity distribution is known.}, keywords = {EAGE, Helmholtz, Iterative, machine learning}, doi = {10.3997/2214-4609.201901542}, url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2019/rizzuti2019EAGElis/rizzuti2019EAGElis.pdf}, presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2019/rizzuti2019EAGElis/rizzuti2019EAGElis_pres.pdf}, author = {Gabrio Rizzuti and Ali Siahkoohi and Felix J. Herrmann} } @article{raissi2018deep, title={Deep hidden physics models: Deep learning of nonlinear partial differential equations}, author={Raissi, Maziar}, journal={The Journal of Machine Learning Research}, volume={19}, number={1}, pages={932--955}, year={2018}, publisher={JMLR. org} } @article{moseley2018fast, title={Fast approximate simulation of seismic waves with deep learning}, author={Moseley, Benjamin and Markham, Andrew and Nissen-Meyer, Tarje}, journal={arXiv preprint arXiv:1807.06873}, year={2018} } @article{doi:10.1190/geo2018-0281.1, author = {Lasse Amundsen and Ørjan Pedersen}, title = {Elimination of temporal dispersion from the finite-difference solutions of wave equations in elastic and anelastic models}, journal = {GEOPHYSICS}, volume = {84}, number = {2}, pages = {T47-T58}, year = {2019}, doi = {10.1190/geo2018-0281.1}, URL = {https://doi.org/10.1190/geo2018-0281.1}, eprint = {https://doi.org/10.1190/geo2018-0281.1}, abstract = {Time integration of wave equations can be carried out with explicit time stepping using a finite-difference (FD) approximation. The wave equation is the partial differential equation that governs the wavefield that is solved for. The FD approximation gives another partial differential equation — the one solved in the computer for the FD wavefield. This approximation to time integration in numerical modeling produces a wavefield contaminated with temporal dispersion, particularly at high frequencies. We find how the Fourier transform can be used to relate the two partial differential equations and their solutions. Each of the two wavefields is then a time-frequency transformation of the other. First, this transformation allows temporal dispersion to be eliminated from the FD wavefield, and second, it allows temporal dispersion to be added to the exact wavefield. The two transforms are band-limited inverse operations. The transforms can be implemented by using time-step independent, noncausal time-varying digital filters that can be precomputed exactly from sums over Bessel functions. Their product becomes the symmetric Toeplitz matrix with the elements defined through the cardinal sine (sinc) function. For anelastic materials, the effect of numerical time dispersion in a wavefield propagating in a medium needs special treatment. Dispersion can be removed by using the time-frequency transform when the FD wavefield is modeled in a medium with the frequency-modified modulus relative to the physical modulus of interest. In the rheological model of the generalized Maxwell body, the frequency-modified modulus is written as a power series, which allows a term-by-term Fourier transform to the time domain. In a low-frequency approximation, the modified modulus obtains the same form as the physical modulus, and it can be implemented as changes in the unrelaxed modulus and shifts of the relaxation frequencies and their strengths of the physical modulus. } } @article{hu2019render4completion, title={Render4Completion: Synthesizing Multi-view Depth Maps for 3D Shape Completion}, author={Hu, Tao and Han, Zhizhong and Shrivastava, Abhinav and Zwicker, Matthias}, journal={arXiv preprint arXiv:1904.08366}, year={2019} } @InProceedings{he2016deep, author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, title = {{D}eep {R}esidual {L}earning for {I}mage {R}ecognition}, DOI = {10.1109/CVPR.2016.90}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2016}, url = {https://ieeexplore.ieee.org/document/7780459}, eprint = {https://ieeexplore.ieee.org/document/7780459}, pages={770--778} } @InProceedings{johnson2016perceptual, author={Johnson, Justin and Alahi, Alexandre and Fei-Fei, Li}, title={{P}erceptual {L}osses for {R}eal-{T}ime {S}tyle {T}ransfer and {S}uper-{R}esolution}, booktitle={Computer Vision -- European Conference on Computer Vision (ECCV) 2016}, year={2016}, publisher={Springer International Publishing}, pages={694--711}, DOI={10.1007/978-3-319-46475-6_43}, url = {https://link.springer.com/chapter/10.1007%2F978-3-319-46475-6_43}, eprint = {https://link.springer.com/chapter/10.1007%2F978-3-319-46475-6_43}, abstract={We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.}, } @article{siahkoohi2019transfer, author={Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J.}, title={The importance of transfer learning in seismic modeling and imaging}, year={2019}, note={Submitted to GEOPHYSICS in February 2019} } @article{siahkoohi2018deep, author = {Siahkoohi, Ali and Louboutin, Mathias and Kumar, Rajiv and Herrmann, Felix J.}, title = {Deep-convolutional neural networks in prestack seismic: Two exploratory examples}, journal = {SEG Technical Program Expanded Abstracts 2018}, chapter = {}, pages = {2196--2200}, year = {2018}, doi = {10.1190/segam2018-2998599.1}, URL = {https://library.seg.org/doi/abs/10.1190/segam2018-2998599.1}, eprint = {https://library.seg.org/doi/pdf/10.1190/segam2018-2998599.1} } @inproceedings{szegedy2017inception, title={{I}nception-v4, {I}nception-{R}es{N}et and the {I}mpact of {R}esidual {C}onnections on {L}earning.}, author={Szegedy, Christian and Ioffe, Sergey and Vanhoucke, Vincent and Alemi, Alexander A}, booktitle={Proceedings of the Thirty-First Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI-17)}, volume={4}, pages={4278--4284}, year={2017}, url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14806}, } @article{ruthotto2018deep, author = {Lars Ruthotto and Eldad Haber}, title = {Deep Neural Networks motivated by Partial Differential Equations}, journal = {CoRR}, volume = {abs/1804.04272}, year = {2018}, url = {http://arxiv.org/abs/1804.04272}, archivePrefix = {arXiv}, eprint = {1804.04272}, timestamp = {Tue, 01 May 2018 19:46:29 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1804-04272}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{paszke2017automatic, title={Automatic differentiation in PyTorch}, author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam}, year={2017} } @article{devito-compiler, author = { {Luporini}, F. and {Lange}, M. and {Louboutin}, M. and {Kukreja}, N. and {H{\"u}ckelheim}, J. and {Yount}, C. and {Witte}, P. and {Kelly}, P.~H.~J. and {Gorman}, G.~J. and {Herrmann}, F.~J. }, title = { Architecture and performance of Devito, a system for automated stencil computation }, journal = { CoRR }, volume = { abs/1807.03032 }, month = { jul }, year = { 2018 }, url = { http://arxiv.org/abs/1807.03032 }, archivePrefix = { arXiv }, eprint = { 1807.03032 } } @article{devito-api, author = { {Louboutin}, M. and {Lange}, M. and {Luporini}, F. and {Kukreja}, N. and {Witte}, P.~A. and {Herrmann}, F.~J. and {Velesko}, P. and {Gorman}, G.~J. }, title = {Devito: an embedded domain-specific language for finite differences and geophysical exploration}, journal = { CoRR }, volume = { abs/1808.01995 }, month = { Aug }, year = { 2018 }, url = { https://arxiv.org/abs/1808.01995 }, archivePrefix = { arXiv }, eprint = { 1808.01995 } } @article{pan2018theoretical, title={Theoretical Analysis of Image-to-Image Translation with Adversarial Learning}, author={Pan, Xudong and Zhang, Mi and Ding, Daizong}, journal={arXiv preprint arXiv:1806.07001}, year={2018} } @inproceedings{yosinski2014transferable, title={How transferable are features in deep neural networks?}, author={Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod}, booktitle={Advances in neural information processing systems}, pages={3320--3328}, year={2014} } @incollection{lewis2017deep, title={Deep learning prior models from seismic images for full-waveform inversion}, author={Lewis, Winston and Vigh, Denes}, booktitle={SEG Technical Program Expanded Abstracts 2017}, pages={1512--1517}, year={2017}, publisher={Society of Exploration Geophysicists} } @article{gupta2018deep, title={Deep Mesh Projectors for Inverse Problems}, author={Gupta, Sidharth and Kothari, Konik and de Hoop, Maarten V and Dokmani{\'c}, Ivan}, journal={arXiv preprint arXiv:1805.11718}, year={2018} } @article{araya2018deep, title={Deep-learning tomography}, author={Araya-Polo, Mauricio and Jennings, Joseph and Adler, Amir and Dahlke, Taylor}, journal={The Leading Edge}, volume={37}, number={1}, pages={58--66}, year={2018}, publisher={Society of Exploration Geophysicists} } @article{richardson2018generative, title={Generative Adversarial Networks for Model Order Reduction in Seismic Full-Waveform Inversion}, author={Richardson, Alan}, journal={arXiv preprint arXiv:1806.00828}, year={2018} } @article{mosser2018stochastic, title={Stochastic seismic waveform inversion using generative adversarial networks as a geological prior}, author={Mosser, Lukas and Dubrule, Olivier and Blunt, Martin J}, journal={arXiv preprint arXiv:1806.03720}, year={2018} } @inproceedings{mikhailiuk2018deep, title={Deep Learning Applied to Seismic Data Interpolation}, author={Mikhailiuk, A and Faul, A}, booktitle={80th EAGE Conference and Exhibition 2018}, year={2018} } @inproceedings{wang2018seismic, title={Seismic Data Interpolation Using Deep Learning Based Residual Networks}, author={Wang, BF and Zhang, N and Lu, WK and Zhang, P and Geng, JH}, booktitle={80th EAGE Conference and Exhibition 2018}, year={2018} } @inproceedings{sun2018low, title={Low frequency extrapolation with deep learning}, author={Sun, Hongyu and Demanet, Laurent}, booktitle={ERL Annual Founding Members Meeting}, year={2018} } @article{lin2016estimation, title={Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps}, author={Lin, Tim TY and Herrmann, Felix J}, journal={Geophysics}, volume={81}, number={3}, pages={V183--V197}, year={2016}, publisher={Society of Exploration Geophysicists} } @inproceedings{siahkoohi2018seismic, title={Seismic Data Reconstruction with Generative Adversarial Networks}, author={Siahkoohi, Ali and Kumar, Rajiv and Herrmann, F}, booktitle={80th EAGE Conference and Exhibition 2018}, year={2018} } @inproceedings{pathak2016context, title={Context encoders: Feature learning by inpainting}, author={Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={2536--2544}, year={2016} } @incollection{lecun2012efficient, title={Efficient backprop}, author={LeCun, Yann A and Bottou, L{\'e}on and Orr, Genevieve B and M{\"u}ller, Klaus-Robert}, booktitle={Neural networks: Tricks of the trade}, pages={9--48}, year={2012}, publisher={Springer} } @article{keskar2016large, title={On large-batch training for deep learning: Generalization gap and sharp minima}, author={Keskar, Nitish Shirish and Mudigere, Dheevatsa and Nocedal, Jorge and Smelyanskiy, Mikhail and Tang, Ping Tak Peter}, journal={arXiv preprint arXiv:1609.04836}, year={2016} } @book{Goodfellow-et-al-2016, title={Deep Learning}, author={Ian Goodfellow and Yoshua Bengio and Aaron Courville}, publisher={MIT Press}, note={\url{http://www.deeplearningbook.org}}, year={2016} } @article{bottou2018optimization, title={Optimization methods for large-scale machine learning}, author={Bottou, L{\'e}on and Curtis, Frank E and Nocedal, Jorge}, journal={SIAM Review}, volume={60}, number={2}, pages={223--311}, year={2018}, publisher={SIAM} } @article{li2017visualizing, title={Visualizing the loss landscape of neural nets}, author={Li, Hao and Xu, Zheng and Taylor, Gavin and Goldstein, Tom}, journal={arXiv preprint arXiv:1712.09913}, year={2017} } @inproceedings{simonyan2014very, title={Very deep convolutional networks for large-scale image recognition}, author={Simonyan, Karen and Zisserman, Andrew}, booktitle={International Conference on Learning Representations}, year={2015} } @inproceedings{yoo2016pixel, title={Pixel-level domain transfer}, author={Yoo, Donggeun and Kim, Namil and Park, Sunggyun and Paek, Anthony S and Kweon, In So}, booktitle={European Conference on Computer Vision}, pages={517--532}, year={2016}, organization={Springer} } @article{zhang2017stackgan, title={Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks}, author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Huang, Xiaolei and Wang, Xiaogang and Metaxas, Dimitris}, journal={arXiv preprint}, year={2017} } @inproceedings{wang2018pix2pixHD, title={High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs}, author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Andrew Tao and Jan Kautz and Bryan Catanzaro}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2018} } @inproceedings{benaim2017one, title={One-sided unsupervised domain mapping}, author={Benaim, Sagie and Wolf, Lior}, booktitle={Advances in neural information processing systems}, pages={752--762}, year={2017} } @inproceedings{wolterink2017deep, title={Deep MR to CT synthesis using unpaired data}, author={Wolterink, Jelmer M and Dinkla, Anna M and Savenije, Mark HF and Seevinck, Peter R and van den Berg, Cornelis AT and I{\v{s}}gum, Ivana}, booktitle={International Workshop on Simulation and Synthesis in Medical Imaging}, pages={14--23}, year={2017}, organization={Springer} } @inproceedings{ledig2017photo, title={Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.}, author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew P and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others}, booktitle={CVPR}, volume={2}, number={3}, pages={4}, year={2017} } @inproceedings{yi2017dualgan, title={DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.}, author={Yi, Zili and Hao (Richard) Zhang and Tan, Ping and Gong, Minglun}, booktitle={ICCV}, pages={2868--2876}, year={2017} } @article{guibas2017synthetic, title={Synthetic Medical Images from Dual Generative Adversarial Networks}, author={Guibas, John T and Virdi, Tejpal S and Li, Peter S}, journal={arXiv preprint arXiv:1709.01872}, year={2017} } @inproceedings{ronneberger2015u, title={U-net: Convolutional networks for biomedical image segmentation}, author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas}, booktitle={International Conference on Medical image computing and computer-assisted intervention}, pages={234--241}, year={2015}, organization={Springer} } @inproceedings{zhu2017toward, title={Toward multimodal image-to-image translation}, author={Zhu, Jun-Yan and Zhang, Richard and Pathak, Deepak and Darrell, Trevor and Efros, Alexei A and Wang, Oliver and Shechtman, Eli}, booktitle={Advances in Neural Information Processing Systems}, pages={465--476}, year={2017} } @article{pix2pix2016, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={arxiv}, year={2016} } @article{yang2018mri, title={MRI Image-to-Image Translation for Cross-Modality Image Registration and Segmentation}, author={Yang, Qianye and Li, Nannan and Zhao, Zixu and Fan, Xingyu and Chang, Eric I and Xu, Yan and others}, journal={arXiv preprint arXiv:1801.06940}, year={2018} } @inproceedings{siahkoohi2018EAGEsdr, title={Seismic data reconstruction with Generative Adversarial Networks}, author={Ali Siahkoohi and Rajiv Kumar and Felix J. Herrmann}, booktitle={80th EAGE Conference and Exhibition 2018}, year={2018} } @article{Shafiq_SeisSal_2018, author = {Muhammad Amir Shafiq and Zhilinh Long and Haibin Di and Ghassan AlRegib}, title = {Attention Models in Perception and Interpretation of Seismic Volumes}, journal = {Geophysics}, year = {2018}, } @article{louboutin2017full, title={Full-waveform inversion, Part 1: Forward modeling}, author={Louboutin, Mathias and Witte, Philipp and Lange, Michael and Kukreja, Navjot and Luporini, Fabio and Gorman, Gerard and Herrmann, Felix J}, journal={The Leading Edge}, volume={36}, number={12}, pages={1033--1036}, year={2017}, publisher={Society of Exploration Geophysicists} } @inproceedings{abadi2016tensorflow, title={TensorFlow: A System for Large-Scale Machine Learning.}, author={Abadi, Mart{\'\i}n and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others}, booktitle={OSDI}, volume={16}, pages={265--283}, year={2016} } @InProceedings{lange2017scipy, author = { {M}. {L}ange and {N}. {K}ukreja and {F}. {L}uporini and {M}. {L}ouboutin and {C}. {Y}ount and {J}. {H}\"uckelheim and {G}. {J}. {G}orman }, title = { {O}ptimised finite difference computation from symbolic equations }, booktitle = { {P}roceedings of the 15th {P}ython in {S}cience {C}onference }, pages = { 89 - 96 }, year = { 2017 }, editor = { {K}aty {H}uff and {D}avid {L}ippa and {D}illon {N}iederhut and {M} {P}acer } } @article{dokmanic2016inverse, title={Inverse problems with invariant multiscale statistics}, author={Dokmani{\'c}, Ivan and Bruna, Joan and Mallat, St{\'e}phane and de Hoop, Maarten}, journal={arXiv preprint arXiv:1609.05502}, year={2016} } @ARTICLE{Goodfellow2014, author = {{Goodfellow}, Ian and {Pouget-Abadie}, Jean and {Mirza}, Mehdi and {Xu}, Bing and {Warde-Farley}, David and {Ozair}, Sherjil and {Courville}, Aaron and {Bengio}, Yoshua}, title = "{Generative Adversarial Nets}", journal = {Advances in neural information processing systems}, year = 2014, pages = {2672-2680} } @article{li2013application, title={Application of the Neural Decision Tree approach for prediction of petroleum production}, author={Li, X and Chan, CW and Nguyen, HH}, journal={Journal of Petroleum science and engineering}, volume={104}, pages={11--16}, year={2013}, publisher={Elsevier} } @inproceedings{sun2016deep, title={Deep ADMM-Net for compressive sensing MRI}, author={Sun, Jian and Li, Huibin and Xu, Zongben and others}, booktitle={Advances in Neural Information Processing Systems}, pages={10--18}, year={2016} } @article{mardani2017recurrent, title={Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery}, author={Mardani, Morteza and Monajemi, Hatef and Papyan, Vardan and Vasanawala, Shreyas and Donoho, David and Pauly, John}, journal={arXiv preprint arXiv:1711.10046}, year={2017} } @article{adler2018learned, title={Learned primal-dual reconstruction}, author={Adler, Jonas and {\"O}ktem, Ozan}, journal={IEEE Transactions on Medical Imaging}, year={2018}, publisher={IEEE} } @article{fai2017inner, title={An inner-loop free solution to inverse problems using deep neural networks}, author={Fai, Kai and Wei, Qi and Carin, Lawrence and Heller, Katherine}, year={2017} } @article{mousavi2016seismic, title={Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression}, author={Mousavi, S Mostafa and Horton, Stephen P and Langston, Charles A and Samei, Borhan}, journal={Geophysical Journal International}, volume={207}, number={1}, pages={29--46}, year={2016}, publisher={Oxford University Press} } @article{rawles2015non, title={A non-parametric method for automatic determination of P-wave and S-wave arrival times: application to local micro earthquakes}, author={Rawles, Christopher and Thurber, Clifford}, journal={Geophysical Journal International}, volume={202}, number={2}, pages={1164--1179}, year={2015}, publisher={Oxford University Press} } @article{zhao2015comparison, title={A comparison of classification techniques for seismic facies recognition}, author={Zhao, Tao and Jayaram, Vikram and Roy, Atish and Marfurt, Kurt J}, journal={Interpretation}, volume={3}, number={4}, pages={SAE29--SAE58}, year={2015}, publisher={Society of Exploration Geophysicists and American Association of Petroleum Geologists} } @article{chen2017automatic, title={Automatic microseismic event picking via unsupervised machine learning}, author={Chen, Yangkang}, journal={Geophysical Journal International}, volume={212}, number={1}, pages={88--102}, year={2017}, publisher={Oxford University Press} } @inproceedings{anderson2016petroleum, title={Petroleum Analytics Learning Machine to Forecast Production in the Wet Gas Marcellus Shale}, author={Anderson, Roger N and Xie, Boyi and Wu, Leon and Kressner, Arthur A and Frantz Jr, Joseph H and Ockree, Matthew A and Brown, Kenneth G}, year={2016}, organization={Unconventional Resources Technology Conference (URTEC)} } @article{brunton2016discovering, title={Discovering governing equations from data by sparse identification of nonlinear dynamical systems}, author={Brunton, Steven L and Proctor, Joshua L and Kutz, J Nathan}, journal={Proceedings of the National Academy of Sciences}, volume={113}, number={15}, pages={3932--3937}, year={2016}, publisher={National Acad Sciences} } @article{aizenberg2016multilayer, title={Multilayer neural network with multi-valued neurons in time series forecasting of oil production}, author={Aizenberg, Igor and Sheremetov, Leonid and Villa-Vargas, Luis and Martinez-Mu{\~n}oz, Jorge}, journal={Neurocomputing}, volume={175}, pages={980--989}, year={2016}, publisher={Elsevier} } @article{bengio2013representation, title={Representation learning: A review and new perspectives}, author={Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={35}, number={8}, pages={1798--1828}, year={2013}, publisher={IEEE} } @inproceedings{szegedy2015going, title={Going deeper with convolutions}, author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={1--9}, year={2015} } @inproceedings{krizhevsky2012imagenet, title={Imagenet classification with deep convolutional neural networks}, author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E}, booktitle={Advances in neural information processing systems}, pages={1097--1105}, year={2012} } @article{hinton2012deep, title={Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups}, author={Hinton, Geoffrey and Deng, Li and Yu, Dong and Dahl, George E and Mohamed, Abdel-rahman and Jaitly, Navdeep and Senior, Andrew and Vanhoucke, Vincent and Nguyen, Patrick and Sainath, Tara N and others}, journal={IEEE Signal Processing Magazine}, volume={29}, number={6}, pages={82--97}, year={2012}, publisher={IEEE} } @ARTICLE{Yeh2016, author = {{Yeh}, R. and {Chen}, C. and {Yian Lim}, T. and {Hasegawa-Johnson}, M. and {Do}, M.~N.}, title = "{Semantic Image Inpainting with Perceptual and Contextual Losses}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1607.07539}, primaryClass = "cs.CV", keywords = {Computer Science - Computer Vision and Pattern Recognition}, year = 2016, month = jul, adsurl = {http://adsabs.harvard.edu/abs/2016arXiv160707539Y}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @ARTICLE{Lin2016, author = {{Lin}, T.~T.~Y. and {Herrmann}, F.~J.}, title = "{Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps}", journal = {Geophysics}, year = 2016, month = may, volume = 81, pages = {V183-V197}, doi = {10.1190/geo2015-0263.1}, adsurl = {http://adsabs.harvard.edu/abs/2016Geop...81V.183L}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @ARTICLE{Spitz1991, title={Seismic trace interpolation in the FX domain}, author={Spitz, Simon}, journal={Geophysics}, volume={56}, number={6}, pages={785--794}, year={1991}, publisher={Society of Exploration Geophysicists} } @ARTICLE{Herrmann2008, title={Non-parametric seismic data recovery with curvelet frames}, author={Herrmann, Felix J and Hennenfent, Gilles}, journal={Geophysical Journal International}, volume={173}, number={1}, pages={233--248}, year={2008}, publisher={Oxford University Press} } @ARTICLE{Kumar2013, title={Reconstruction of seismic wavefields via low-rank matrix factorization in the hierarchical-separable matrix representation}, author={Kumar, Rajiv and Mansour, Hassan and Herrmann, Felix J and Aravkin, Aleksandr Y}, booktitle={SEG 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