Fetching the paper…
Reading the bibliography…
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand engineering.
Deep learning-aided trainable projected gradient decoding for ldpc codes
Tadashi Wadayama and Satoshi Takabe · 1901
Earlier work this paper cites.
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld, Meirav Galun, Ron Kimmel, Irad Yavneh, and Ronen Basri · 1902
Earlier work this paper cites.
Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, and Ekin D Cubuk · 1906
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
Earlier work this paper cites.
Theoretical interpretation of learned step size in deep-unfolded gradient descent
Satoshi Takabe and Tadashi Wadayama · 2001
Earlier work this paper cites.
Ada-lista: Learned solvers adaptive to varying models
Aviad Aberdam, Alona Golts, and Michael Elad · 2001
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
Earlier work this paper cites.
Safeguarded learned convex optimization
Howard Heaton, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2003
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Yurii Nesterov · 2005
Earlier work this paper cites.
A non-local algorithm for image denoising
Antoni Buades, Bartomeu Coll, and J-M Morel · 2005
Earlier work this paper cites.
Guarantees for tuning the step size using a learning-to-learn approach
Xiang Wang, Shuai Yuan, Chenwei Wu, and Rong Ge · 2006
Earlier work this paper cites.
Learning to stop while learning to predict
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, and Le Song · 2006
Earlier work this paper cites.
K-svd: An algorithm for designing overcomplete dictionaries for sparse representation
Michal Aharon, Michael Elad, and Alfred Bruckstein · 2006
Earlier work this paper cites.
Scalable plug-and-play admm with convergence guarantees
Yu Sun, Zihui Wu, Brendt Wohlberg, and Ulugbek S Kamilov · 2006
Earlier work this paper cites.
Understanding deep architectures with reasoning layer
Xinshi Chen, Yufei Zhang, Christoph Reisinger, and Le Song · 2006
Earlier work this paper cites.
Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
Earlier work this paper cites.
Learnable descent algorithm for nonsmooth nonconvex image reconstruction
Yunmei Chen, Hongcheng Liu, Xiaojing Ye, and Qingchao Zhang · 2007
Earlier work this paper cites.
Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
Earlier work this paper cites.
Projecting to manifolds via unsupervised learning
Howard Heaton, Samy Wu Fung, Alex Tong Lin, Stanley Osher, and Wotao Yin · 2008
Earlier work this paper cites.
Iterative thresholding for sparse approximations
Thomas Blumensath and Mike E Davies · 2008
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
Training stronger baselines for learning to optimize
Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, and Zhangyang Wang · 2010
Earlier work this paper cites.
Swarmdock and the use of normal modes in protein-protein docking
Iain H Moal and Paul A Bates · 2010
Earlier work this paper cites.
Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, and Ulugbek S Kamilov · 2010
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Learning efficient structured sparse models
Pablo Sprechmann, Alex Bronstein, and Guillermo Sapiro · 2012
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Earlier work this paper cites.
Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
Chris Thornton, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
Earlier work this paper cites.
Supervised sparse analysis and synthesis operators
Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M Bronstein, and Guillermo Sapiro · 2013
Earlier work this paper cites.
Bilevel sparse models for polyphonic music transcription
Tal Ben Yakar, Roee Litman, Pablo Sprechmann, Alexander M Bronstein, and Guillermo Sapiro · 2013
Earlier work this paper cites.
Supervised descent method and its applications to face alignment
Xuehan Xiong and Fernando De la Torre · 2013
Earlier work this paper cites.
A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion
Yangyang Xu and Wotao Yin · 2013
Earlier work this paper cites.
Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
Earlier work this paper cites.
Input warping for bayesian optimization of non-stationary functions
Jasper Snoek, Kevin Swersky, Rich Zemel, and Ryan Adams · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Flexisp: A flexible camera image processing framework
Felix Heide, Markus Steinberger, Yun-Ta Tsai, Mushfiqur Rouf, Dawid Pajak, Dikpal Reddy, Orazio Gallo, Jing Liu, Wolfgang Heidrich, Karen Egiazarian, et al · 2014
Earlier work this paper cites.
Christoph Studer, Tom Goldstein, Wotao Yin, and Richard G Baraniuk · 2014
Earlier work this paper cites.
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
John R. Hershey, Jonathan Le Roux, and Felix Weninger · 2014
Earlier work this paper cites.
Supervised non-euclidean sparse nmf via bilevel optimization with applications to speech enhancement
P. Sprechmann, A. M. Bronstein, and G. Sapiro · 2014
Earlier work this paper cites.
Bm3d-amp: A new image recovery algorithm based on bm3d denoising
Christopher A Metzler, Arian Maleki, and Richard G Baraniuk · 2015
Earlier work this paper cites.
Learning Efficient Sparse and Low Rank Models
P. Sprechmann, A. M. Bronstein, and G. Sapiro · 2015
Earlier work this paper cites.
Conditional random fields as recurrent neural networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Earlier work this paper cites.
Derivative-free optimization via classification
Yang Yu, Hong Qian, and Yi-Qi Hu · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Earlier work this paper cites.
Poisson inverse problems by the plug-and-play scheme
Arie Rond, Raja Giryes, and Michael Elad · 2016
Earlier work this paper cites.
Turning a denoiser into a super-resolver using plug and play priors
Alon Brifman, Yaniv Romano, and Michael Elad · 2016
Earlier work this paper cites.
Plug-and-play priors for bright field electron tomography and sparse interpolation
Suhas Sreehari, S Venkat Venkatakrishnan, Brendt Wohlberg, Gregery T Buzzard, Lawrence F Drummy, Jeffrey P Simmons, and Charles A Bouman · 2016
Earlier work this paper cites.
Plug-and-play admm for image restoration: Fixed-point convergence and applications
Stanley H Chan, Xiran Wang, and Omar A Elgendy · 2016
Earlier work this paper cites.
Maximal sparsity with deep networks?
Bo Xin, Yizhou Wang, Wen Gao, David Wipf, and Baoyuan Wang · 2016
Earlier work this paper cites.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Yunjin Chen and Thomas Pock · 2016
Earlier work this paper cites.
Learning a task-specific deep architecture for clustering
Zhangyang Wang, Shiyu Chang, Jiayu Zhou, Meng Wang, and Thomas S Huang · 2016
Earlier work this paper cites.
Onsager-corrected deep learning for sparse linear inverse problems
Mark Borgerding and Philip Schniter · 2016
Earlier work this paper cites.
Deep admm-net for compressive sensing mri
Jian Sun, Huibin Li, Zongben Xu, et al · 2016
Earlier work this paper cites.
Deep subspace clustering with sparsity prior
Xi Peng, Shijie Xiao, Jiashi Feng, Wei-Yun Yau, and Zhang Yi · 2016
Earlier work this paper cites.
Learning to learn without gradient descent by gradient descent
Yutian Chen, Matthew W Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P Lillicrap, Matt Botvinick, and Nando Freitas · 2017
Earlier work this paper cites.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Earlier work this paper cites.
Amp-inspired deep networks for sparse linear inverse problems
Mark Borgerding, Philip Schniter, and Sundeep Rangan · 2017
Earlier work this paper cites.
Fast bayesian optimization of machine learning hyperparameters on large datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 2017
Earlier work this paper cites.
Learning gradient descent: Better generalization and longer horizons
Kaifeng Lv, Shunhua Jiang, and Jian Li · 2017
Earlier work this paper cites.
Learned optimizers that scale and generalize, 2017
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V. Le · 2017
Earlier work this paper cites.
Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
Earlier work this paper cites.
Parameter-free plug-and-play admm for image restoration
Xiran Wang and Stanley H Chan · 2017
Earlier work this paper cites.
Primal-dual plug-and-play image restoration
Shunsuke Ono · 2017
Earlier work this paper cites.
A plug-and-play priors approach for solving nonlinear imaging inverse problems
Ulugbek S Kamilov, Hassan Mansour, and Brendt Wohlberg · 2017
Earlier work this paper cites.
Scene-adapted plug-and-play algorithm with convergence guarantees
Afonso M Teodoro, José M Bioucas-Dias, and Mário AT Figueiredo · 2017
Earlier work this paper cites.
Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
Tim Meinhardt, Michael Moller, Caner Hazirbas, and Daniel Cremers · 2017
Earlier work this paper cites.
One network to solve them all–solving linear inverse problems using deep projection models
JH Rick Chang, Chun-Liang Li, Barnabas Poczos, BVK Vijaya Kumar, and Aswin C Sankaranarayanan · 2017
Cited alongside, same era.
Learning deep cnn denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
Cited alongside, same era.
Deep mean-shift priors for image restoration
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, and Meiguang Jin · 2017
Cited alongside, same era.
Learning the weight matrix for sparsity averaging in compressive imaging
Dimitris Perdios, Adrien Besson, Philippe Rossinelli, and Jean-Philippe Thiran · 2017
Cited alongside, same era.
Convolutional neural networks analyzed via convolutional sparse coding
Vardan Papyan, Yaniv Romano, and Michael Elad · 2017
Cited alongside, same era.
Solving ill-posed inverse problems using iterative deep neural networks
Latent space physics: Towards learning the temporal evolution of fluid flow
Steffen Wiewel, Moritz Becher, and Nils Thuerey · 2019
Later among the works it cites.
Trainable ista for sparse signal recovery
Daisuke Ito, Satoshi Takabe, and Tadashi Wadayama · 2019
Later among the works it cites.
Sure-tista: A signal recovery network for compressed sensing
Mengcheng Yao, Jian Dang, Zaichen Zhang, and Liang Wu · 2019
Later among the works it cites.
Differentiable linearized admm
Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, and Zhouchen Lin · 2019
Later among the works it cites.
Model learning: Primal dual networks for fast mr imaging
Jing Cheng, Haifeng Wang, Leslie Ying, and Dong Liang · 2019
Later among the works it cites.
Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network
Zichao Long, Yiping Lu, and Bin Dong · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonas Adler and Ozan Öktem · 2017
Cited alongside, same era.
Recurrent inference machines for solving inverse problems
Patrick Putzky and Max Welling · 2017
Cited alongside, same era.
Learned d-amp: Principled neural network based compressive image recovery
Chris Metzler, Ali Mousavi, and Richard Baraniuk · 2017
Cited alongside, same era.
Orthogonal amp
Junjie Ma and Li Ping · 2017
Cited alongside, same era.
Variational networks: connecting variational methods and deep learning
Erich Kobler, Teresa Klatzer, Kerstin Hammernik, and Thomas Pock · 2017
Cited alongside, same era.
A deep learning architecture for limited-angle computed tomography reconstruction
Kerstin Hammernik, Tobias Würfl, Thomas Pock, and Andreas Maier · 2017
Cited alongside, same era.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Cited alongside, same era.
Controlling neural networks via energy dissipation
Michael Moeller, Thomas Mollenhoff, and Daniel Cremers · 2019
Later among the works it cites.
Hyperadam: A learnable task-adaptive adam for network training
Shipeng Wang, Jian Sun, and Zongben Xu · 2019
Later among the works it cites.
Degrees of freedom analysis of unrolled neural networks
Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, and David Donoho · 2019
Later among the works it cites.
Jpeg artifacts reduction via deep convolutional sparse coding
Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, and John Paisley · 2019
Later among the works it cites.
Unsupervised deep basis pursuit: Learning reconstruction without ground-truth data
Jonathan I Tamir, X Yu Stella, and Michael Lustig · 2019
Later among the works it cites.
Deep unfolded robust pca with application to clutter suppression in ultrasound
Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, Qiong He, Jianwen Luo, Ruud JG van Sloun, and Yonina C Eldar · 2019
Later among the works it cites.
Deep-learning inversion: A next-generation seismic velocity model building method
Fangshu Yang and Jianwei Ma · 2019
Later among the works it cites.
Deep iterative reconstruction for phase retrieval
Çağatay Işıl, Figen S Oktem, and Aykut Koç · 2019
Later among the works it cites.
Unrolled projected gradient descent for multi-spectral image fusion
Suhas Lohit, Dehong Liu, Hassan Mansour, and Petros T Boufounos · 2019
Later among the works it cites.
Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
Later among the works it cites.
Deep network classification by scattering and homotopy dictionary learning
John Zarka, Louis Thiry, Tomás Angles, and Stéphane Mallat · 2019
Later among the works it cites.
Deep null space learning for inverse problems: convergence analysis and rates
Johannes Schwab, Stephan Antholzer, and Markus Haltmeier · 2019
Later among the works it cites.
Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Dong Liang, Jing Cheng, Ziwen Ke, and Leslie Ying · 2020
Later among the works it cites.
Iterative amortized policy optimization
Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, and Yisong Yue · 2020
Later among the works it cites.
Calibration of shared equilibria in general sum partially observable markov games
Nelson Vadori, Sumitra Ganesh, Prashant Reddy, and Manuela Veloso · 2020
Later among the works it cites.
Better software analytics via “duo”: Data mining algorithms using/used-by optimizers
Amritanshu Agrawal, Tim Menzies, Leandro L Minku, Markus Wagner, and Zhe Yu · 2020
Later among the works it cites.
Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
Later among the works it cites.
Mate: Plugging in model awareness to task embedding for meta learning
Xiaohan Chen, Siyu Tang, Krikamol Muandet, et al · 2020
Later among the works it cites.
Deepcode: Feedback codes via deep learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath · 2020
Later among the works it cites.
Scheduling with predictions and the price of misprediction
Michael Mitzenmacher · 2020
Later among the works it cites.
Nir Shlezinger, Jay Whang, Yonina C Eldar, and Alexandros G Dimakis · 2020
Later among the works it cites.
Learning mixed-integer convex optimization strategies for robot planning and control
Abhishek Cauligi, Preston Culbertson, Bartolomeo Stellato, Dimitris Bertsimas, Mac Schwager, and Marco Pavone · 2020
Later among the works it cites.
Improved adversarial training via learned optimizer, 2020
Yuanhao Xiong and Cho-Jui Hsieh · 2020
Later among the works it cites.
The two regimes of deep network training
Guillaume Leclerc and Aleksander Madry · 2020
Later among the works it cites.
The large learning rate phase of deep learning: the catapult mechanism
Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, and Guy Gur-Ari · 2020
Later among the works it cites.
Jingfeng Wu, Difan Zou, Vladimir Braverman, and Quanquan Gu · 2020
Later among the works it cites.
Automl-zero: evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David So, and Quoc Le · 2020
Later among the works it cites.
Plug-and-play algorithms for large-scale snapshot compressive imaging
Xin Yuan, Yang Liu, Jinli Suo, and Qionghai Dai · 2020
Later among the works it cites.
Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery
Rizwan Ahmad, Charles A Bouman, Gregery T Buzzard, Stanley Chan, Sizhuo Liu, Edward T Reehorst, and Philip Schniter · 2020
Later among the works it cites.
A new recurrent plug-and-play prior based on the multiple self-similarity network
Guangxiao Song, Yu Sun, Jiaming Liu, Zhijie Wang, and Ulugbek S Kamilov · 2020
Later among the works it cites.
Plug-and-play ista converges with kernel denoisers
Ruturaj G Gavaskar and Kunal N Chaudhury · 2020
Later among the works it cites.
Provable convergence of plug-and-play priors with mmse denoisers
Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, and Ulugbek S Kamilov · 2020
Later among the works it cites.
Tuning-free plug-and-play proximal algorithm for inverse imaging problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schnlieb, and Hua Huang · 2020
Later among the works it cites.
Regularization by denoising via fixed-point projection (red-pro)
Regev Cohen, Michael Elad, and Peyman Milanfar · 2020
Later among the works it cites.
Learning to solve tv regularised problems with unrolled algorithms
Hamza Cherkaoui, Jeremias Sulam, and Thomas Moreau · 2020
Later among the works it cites.
Deep unfolding of a proximal interior point method for image restoration
Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, and Marco Prato · 2020
Later among the works it cites.
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, and Carola-Bibiane Schönlieb · 2020
Later among the works it cites.
Neural networks-based regularization for large-scale medical image reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter, Marc Kachelrieß, Marc Dewey, Christian Wald, and Christoph Kolbitsch · 2020
Later among the works it cites.
Complex trainable ista for linear and nonlinear inverse problems
Satoshi Takabe, Tadashi Wadayama, and Yonina C Eldar · 2020
Later among the works it cites.
Model-driven deep learning for mimo detection
Hengtao He, Chao-Kai Wen, Shi Jin, and Geoffrey Ye Li · 2020
Later among the works it cites.
A deep primal-dual proximal network for image restoration
Mingyuan Jiu and Nelly Pustelnik · 2020
Later among the works it cites.
Learned greedy method (lgm): A novel neural architecture for sparse coding and beyond
Rajaei Khatib, Dror Simon, and Michael Elad · 2020
Later among the works it cites.
Data consistent ct reconstruction from insufficient data with learned prior images
Yixing Huang, Alexander Preuhs, Michael Manhart, Guenter Lauritsch, and Andreas Maier · 2020
Later among the works it cites.
Total deep variation: A stable regularizer for inverse problems
Erich Kobler, Alexander Effland, Karl Kunisch, and Thomas Pock · 2020
Later among the works it cites.
Learning the step-size policy for the limited-memory broyden-fletcher-goldfarb-shanno algorithm
Lucas N Egidio, Anders Hansson, and Bo Wahlberg · 2020
Later among the works it cites.
Meta-lr-schedule-net: Learned lr schedules that scale and generalize
Jun Shu, Yanwen Zhu, Qian Zhao, Deyu Meng, and Zongben Xu · 2020
Later among the works it cites.
L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2020
Later among the works it cites.
Learned sparcom: Unfolded deep super-resolution microscopy
Gili Dardikman-Yoffe and Yonina C Eldar · 2020
Later among the works it cites.
Unfolding wmmse using graph neural networks for efficient power allocation
Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, and Santiago Segarra · 2020
Later among the works it cites.
Can learning from natural image denoising be used for seismic data interpolation?
Hao Zhang, Xiuyan Yang, and Jianwei Ma · 2020
Later among the works it cites.
Glad: Learning sparse graph recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, and Le Song · 2020
Later among the works it cites.
Luke Metz, Niru Maheswaranathan, C Daniel Freeman, Ben Poole, and Jascha Sohl-Dickstein · 2020
Later among the works it cites.
Generalization bounds for deep thresholding networks
Arash Behboodi, Holger Rauhut, and Ekkehard Schnoor · 2020
Later among the works it cites.
Huynh Van Luong, Boris Joukovsky, and Nikos Deligiannis · 2020
Later among the works it cites.
End-to-end sequential sampling and reconstruction for mr imaging
Tianwei Yin, Zihui Wu, He Sun, Adrian V Dalca, Yisong Yue, and Katherine L Bouman · 2021
Closest in time.
Consensus multiplicative weights update: Learning to learn using projector-based game signatures
Nelson Vadori, Rahul Savani, Thomas Spooner, and Sumitra Ganesh · 2021
Closest in time.
Automl: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2021
Closest in time.
The voice of optimization
Dimitris Bertsimas and Bartolomeo Stellato · 2021
Closest in time.
A generalizable approach to learning optimizers
Diogo Almeida, Clemens Winter, Jie Tang, and Wojciech Zaremba · 2021
Closest in time.
Learning a minimax optimizer: A pilot study
Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, and Zhangyang Wang · 2021
Closest in time.
Enhanced convergent pnp algorithms for image restoration
Matthieu Terris, Audrey Repetti, Jean-Christophe Pesquet, and Yves Wiaux · 2021
Closest in time.
Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems
Benoît Malézieux, Thomas Moreau, and Matthieu Kowalski · 2021
Closest in time.
Hcgm-net: A deep unfolding network for financial index tracking
Ruben Pauwels, Evaggelia Tsiligianni, and Nikos Deligiannis · 2021
Closest in time.
Neurally augmented alista
Freya Behrens, Jonathan Sauder, and Peter Jung · 2021
Closest in time.
A design space study for lista and beyond
Tianjian Meng, Xiaohan Chen, Yifan Jiang, and Zhangyang Wang · 2021
Closest in time.
Velocity model building with a modified fully convolutional network
Wenlong Wang, Fangshu Yang, and Jianwei Ma · 2090
Closest in time.