Fetching the paper…
Reading the bibliography…
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
K. Pearson, “Liii. on lines and planes of closest fit to systems of points in space,”
1901
Earlier work this paper cites.
C. Eckart and G. Young, “The approximation of one matrix by another of lower rank,”
1936
Earlier work this paper cites.
P. H. Schönemann, “A generalized solution of the orthogonal Procrustes problem,”
1966
Earlier work this paper cites.
J. MacQueen
1967
Earlier work this paper cites.
W. H. Lawton and E. A. Sylvestre, “Self modeling curve resolution,”
1971
Earlier work this paper cites.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,”
1987
Earlier work this paper cites.
M. Turk and A. Pentland, “Eigenfaces for recognition,”
1991
Earlier work this paper cites.
D. W. Tufts and A. A. Shah, “Estimation of a signal waveform from noisy data using low-rank approximation to a data matrix,”
1993
Earlier work this paper cites.
P. Comon, “Independent component analysis, a new concept?”
1994
Earlier work this paper cites.
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,”
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
M. E. Tipping and C. M. Bishop, “Probabilistic principal component analysis,”
1999
Earlier work this paper cites.
M. A. O. Vasilescu and D. Terzopoulos, “Multilinear analysis of image ensembles: Tensorfaces,” in
2002
Earlier work this paper cites.
P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay, “Clustering large graphs via the singular value decomposition,”
2004
Earlier work this paper cites.
A. I. Zecevic and D. D. Siljak, “Global low-rank enhancement of decentralized control for large-scale systems,”
2005
Earlier work this paper cites.
Z. Yuan and E. Oja, “Projective nonnegative matrix factorization for image compression and feature extraction,” in
2005
Earlier work this paper cites.
C. Ding, X. He, and H. D. Simon, “On the equivalence of nonnegative matrix factorization and spectral clustering,” in
2005
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,”
2007
Earlier work this paper cites.
E. J. Candes and B. Recht, “Exact low-rank matrix completion via convex optimization,” in
2008
Earlier work this paper cites.
C. H. Ding, T. Li, and M. I. Jordan, “Convex and semi-nonnegative matrix factorizations,”
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton
2009
Earlier work this paper cites.
A. Cichocki and A.-H. Phan, “Fast local algorithms for large scale nonnegative matrix and tensor factorizations,”
2009
Cited alongside, same era.
H. Ji, C. Liu, Z. Shen, and Y. Xu, “Robust video denoising using low-rank matrix completion,” in
2010
Cited alongside, same era.
H. Xu, C. Caramanis, and S. Sanghavi, “Robust pca via outlier pursuit,”
2010
Cited alongside, same era.
M. O’Toole and K. N. Kutulakos, “Optical computing for fast light transport analysis.”
2010
Cited alongside, same era.
A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: Recovering low-rank and sparse matrices from compressive measurements.” in
2011
Cited alongside, same era.
X. Chen, Z. Han, Y. Wang, Q. Zhao, D. Meng, and Y. Tang, “Robust tensor factorization with unknown noise,” in
2016
Later among the works it cites.
B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural rgb images,” in
2016
Later among the works it cites.
Y. Wang, J. Peng, Q. Zhao, Y. Leung, X.-L. Zhao, and D. Meng, “Hyperspectral image restoration via total variation regularized low-rank tensor decomposition,”
2017
Later among the works it cites.
Q. Xie, Q. Zhao, D. Meng, and Z. Xu, “Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery,”
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Hitomi, J. Gu, M. Gupta, T. Mitsunaga, and S. K. Nayar, “Video from a single coded exposure photograph using a learned over-complete dictionary,” in
2011
Cited alongside, same era.
X. Liang, X. Ren, Z. Zhang, and Y. Ma, “Repairing sparse low-rank texture,” in
2012
Cited alongside, same era.
P. Jain, P. Netrapalli, and S. Sanghavi, “Low-rank matrix completion using alternating minimization,” in
2013
Cited alongside, same era.
P. Benner and T. Breiten, “Low-rank methods for a class of generalized lyapunov equations and related issues,”
2013
Cited alongside, same era.
H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,”
2013
Cited alongside, same era.
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle
2013
Cited alongside, same era.
D. Goldfarb and Z. Qin, “Robust low-rank tensor recovery: Models and algorithms,”
2014
Cited alongside, same era.
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,”
2017
Later among the works it cites.
I. Choi, D. S. Jeon, G. Nam, D. Gutierrez, and M. H. Kim, “High-quality hyperspectral reconstruction using a spectral prior,”
2017
Later among the works it cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Bahri, Y. Panagakis, and S. Zafeiriou, “Robust kronecker component analysis,”
2018
Later among the works it cites.
2018
Later among the works it cites.
K. Gong, C. Catana, J. Qi, and Q. Li, “PET image reconstruction using deep image prior,”
2018
Later among the works it cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,”
2018
Later among the works it cites.
M. Aittala, P. Sharma, A. Yedidia, L. Murmann, W. Freeman, G. Wornell, and F. Durand, “Computational mirrors: Blind inverse light transport by deep matrix factorization,” in
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in
2019
Later among the works it cites.
V. Saragadam and A. Sankaranarayanan, “KRISM—Krylov subspace-based optical computing of hyperspectral images,”
2019
Later among the works it cites.
X.-L. Zhao, W.-H. Xu, T.-X. Jiang, Y. Wang, and M. K. Ng, “Deep plug-and-play prior for low-rank tensor completion,”
2020
Later among the works it cites.
Z. Ke, W. Huang, J. Cheng, Z. Cui, S. Jia, H. Wang, X. Liu, H. Zheng, L. Ying, Y. Zhu
2020
Later among the works it cites.
S. Barratt, Y. Dong, and S. Boyd, “Low-rank forecasting,”
2021
Later among the works it cites.
J. Bacca, Y. Fonseca, and H. Arguello, “Compressive spectral image reconstruction using deep prior and low-rank tensor representation,”
2021
Later among the works it cites.