Fetching the paper…
Reading the bibliography…
In "Dictionary Learning" one tries to recover incoherent matrices $A^* \in \mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are assumed to be normalized) and sparse vectors $x^* \in \mathbb{R}^h$ with a small support of size $h^p$ for some $0 <p < 1$ while having access to observations $y \in \mathbb{R}^n$ where $y = A^*x^*$.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B. A. Olshausen and D. J. Field · 1996
Earlier work this paper cites.
Sparse coding with an overcomplete basis set: A strategy employed by v1?
B. A. Olshausen and D. J. Field · 1997
Earlier work this paper cites.
How close are we to understanding v1?
B. A. Olshausen and D. J. Field · 2005
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
Earlier work this paper cites.
Settling the polynomial learnability of mixtures of gaussians
A. Moitra and G. Valiant · 2010
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
Earlier work this paper cites.
The importance of encoding versus training with sparse coding and vector quantization
A. Coates and A. Y. Ng · 2011
Earlier work this paper cites.
Sparse autoencoder
A. Ng · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio · 2011
Earlier work this paper cites.
Autoencoders, unsupervised learning, and deep architectures
P. Baldi · 2012
Earlier work this paper cites.
Exact recovery of sparsely-used dictionaries
D. A. Spielman, H. Wang, and J. Wright · 2012
Earlier work this paper cites.
Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G. Alain, and P. Vincent · 2013
Earlier work this paper cites.
Building high-level features using large scale unsupervised learning
Q. V. Le · 2013
Earlier work this paper cites.
A. Makhzani and B. Frey · 2013
Cited alongside, same era.
Learning sparsely used overcomplete dictionaries
A. Agarwal, A. Anandkumar, P. Jain, P. Netrapalli, and R. Tandon · 2014
Cited alongside, same era.
What regularized auto-encoders learn from the data-generating distribution
G. Alain and Y. Bengio · 2014
Cited alongside, same era.
Tensor decompositions for learning latent variable models
A. Anandkumar, R. Ge, D. J. Hsu, S. M. Kakade, and M. Telgarsky · 2014
Cited alongside, same era.
More algorithms for provable dictionary learning
S. Arora, A. Bhaskara, R. Ge, and T. Ma · 2014
Cited alongside, same era.
New algorithms for learning incoherent and overcomplete dictionaries
An improved analysis of the er-spud dictionary learning algorithm
J. Błasiok and J. Nelson · 2016
Later among the works it cites.
Sparseness analysis in the pretraining of deep neural networks
J. Li, T. Zhang, W. Luo, J. Yang, X.-T. Yuan, and J. Zhang · 2016
Later among the works it cites.
The landscape of empirical risk for non-convex losses
S. Mei, Y. Bai, and A. Montanari · 2016
Later among the works it cites.
Convolutional neural networks analyzed via convolutional sparse coding
P. Vardan, Y. Romano, and M. Elad · 2016
Later among the works it cites.
Natasha 2:faster non-convex optimization than sgd
Z. Allen-Zhu · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Arora, R. Ge, and A. Moitra · 2014
Cited alongside, same era.
Provable methods for training neural networks with sparse connectivity
H. Sedghi and A. Anandkumar · 2014
Cited alongside, same era.
Simple, efficient, and neural algorithms for sparse coding
S. Arora, R. Ge, T. Ma, and A. Moitra · 2015
Cited alongside, same era.
Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
M. Janzamin, H. Sedghi, and A. Anandkumar · 2015
Cited alongside, same era.
Winner-take-all autoencoders
A. Makhzani and B. J. Frey · 2015
Cited alongside, same era.
On the computational intractability of exact and approximate dictionary learning
A. M. Tillmann · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
Cited alongside, same era.
Closest in time.
Compressed sensing using generative models
A. Bora, A. Jalal, E. Price, and A. G. Dimakis · 2017
Closest in time.
When is a convolutional filter easy to learn?
S. S. Du, J. D. Lee, and Y. Tian · 2017
Closest in time.
No spurious local minima in nonconvex low rank problems: A unified geometric analysis
R. Ge, C. Jin, and Y. Zheng · 2017
Closest in time.
Cbms conference on sparse approximation and signal recovery algorithms, may 22-26, 2017 and 16th new mexico analysis seminar, may 21
A. Gilbert · 2017
Closest in time.
Towards understanding the invertibility of convolutional neural networks
A. C. Gilbert, Y. Zhang, K. Lee, Y. Zhang, and H. Lee · 2017
Closest in time.
Convergence analysis of two-layer neural networks with relu activation
Y. Li and Y. Yuan · 2017
Closest in time.
Y. Tian · 2017
Closest in time.
Towards understanding generalization of deep learning: Perspective of loss landscapes
L. Wu, Z. Zhu, et al · 2017
Closest in time.
Electron-proton dynamics in deep learning
Q. Zhang, R. Panigrahy, S. Sachdeva, and A. Rahimi · 2017
Closest in time.