Vardan Papyan
Vardan Papyan
Assistant Professor, Department of Mathematics, University of Toronto
Verified email at - Homepage
Cited by
Cited by
Convolutional neural networks analyzed via convolutional sparse coding
V Papyan, Y Romano, M Elad
The Journal of Machine Learning Research 18 (1), 2887-2938, 2017
Multi-scale patch-based image restoration
V Papyan, M Elad
IEEE Transactions on image processing 25 (1), 249-261, 2015
Convolutional dictionary learning via local processing
V Papyan, Y Romano, J Sulam, M Elad
Proceedings of the IEEE International Conference on Computer Vision, 5296-5304, 2017
Multilayer convolutional sparse modeling: Pursuit and dictionary learning
J Sulam, V Papyan, Y Romano, M Elad
IEEE Transactions on Signal Processing 66 (15), 4090-4104, 2018
Working locally thinking globally: Theoretical guarantees for convolutional sparse coding
V Papyan, J Sulam, M Elad
IEEE Transactions on Signal Processing 65 (21), 5687-5701, 2017
Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks
V Papyan, Y Romano, J Sulam, M Elad
IEEE Signal Processing Magazine 35 (4), 72-89, 2018
Neural proximal gradient descent for compressive imaging
M Mardani, Q Sun, S Vasawanala, V Papyan, H Monajemi, J Pauly, ...
arXiv preprint arXiv:1806.03963, 2018
Recurrent generative adversarial networks for proximal learning and automated compressive image recovery
M Mardani, H Monajemi, V Papyan, S Vasanawala, D Donoho, J Pauly
arXiv preprint arXiv:1711.10046, 2017
The full spectrum of deepnet hessians at scale: Dynamics with sgd training and sample size
V Papyan
arXiv preprint arXiv:1811.07062, 2018
Measurements of three-level hierarchical structure in the outliers in the spectrum of deepnet hessians
V Papyan
arXiv preprint arXiv:1901.08244, 2019
Prevalence of neural collapse during the terminal phase of deep learning training
V Papyan, XY Han, DL Donoho
Proceedings of the National Academy of Sciences 117 (40), 24652-24663, 2020
Degrees of freedom analysis of unrolled neural networks
M Mardani, Q Sun, V Papyan, S Vasanawala, J Pauly, D Donoho
arXiv preprint arXiv:1906.03742, 2019
Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra
V Papyan
Journal of Machine Learning Research 21 (252), 1-64, 2020
Multimodal latent variable analysis
V Papyan, R Talmon
Signal Processing 142, 178-187, 2018
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
XY Han, V Papyan, DL Donoho
arXiv preprint arXiv:2106.02073, 2021
AI and the Digitized Photoarchive: Promoting Access and Discoverability
E Prokop, XY Han, V Papyan, DL Donoho, CR Johnson Jr
Art Documentation: Journal of the Art Libraries Society of North America 40 …, 2021
Global Versus Local Modeling of Signals
V Papyan, M Elad
Computer Science Department, Technion, 2017
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
L Thresholding, MLCSC Model, V Papyan, Y Romano, M Elad
Theoretical Foundations of Deep Learning via Sparse Representations
V Papyan, Y Romano, J Sulam, M Elad
Working Locally Thinking Globally: Guarantees for Convolutional Sparse Coding
V Papyan, J Sulam, M Elad
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