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Dropout is a popular regularization technique in deep learning.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 1958
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Lower bounds on the maximum cross correlation of signals
L. Welch · 1974
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Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Neural networks and principal component analysis: Learning from examples without local minima
P. Baldi and K. Hornik · 1989
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A simple weight decay can improve generalization
Anders Krogh and John A. Hertz · 1992
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Automatic learning rate maximization by on-line estimation of the hessian’s eigenvectors
Yann LeCun, Patrice Y. Simard, and Barak Pearlmutter · 1993
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Building a large annotated corpus of english: The penn treebank
M. P. Marcus, M. A. Marcinkiewicz, and Santorini B · 1993
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Frames, bases and group representations
Deguang Han and David R. Larson · 2000
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Equal-norm tight frames with erasures
Peter G. Casazza and Jelena Kovačević · 2003
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An introduction to frames and riesz bases
Ole Christensen · 2003
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Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 \ell^{1} minimization
D.L. Donoho and M. Elad · 2003
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Frames, graphs and erasures
B. G. Bodmann and V. I. Paulsen · 2005
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Optimum sequence multisets for synchronous code-division multiple-access channels
M. Rupf and J. L. Massey · 2006
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Optimized projections for compressed sensing
M. Elad · 2007
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
M. Ranzato, F. J. Huang, Y. Boureau, and Y. LeCun · 2007
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Frames for linear reconstruction without phase
Bernhard G. Bodmann, Pete Casazza, and Radu Balan · 2008
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Robustness of fusion frames under erasures of subspaces and of local frame vectors
Pete G. Casazza and Gitta Kutyniok · 2008
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Fusion frames and distributed processing
Peter G Casazza, Gitta Kutyniok, and Shidong Li · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization
J. M. Duarte-Carvajalino and G. Sapiro · 2009
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Asymptotic behaviour of random vandermonde matrices with entries on the unit circle
Øyvind Ryan and Mérouane Debbah · 2009
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Finite tight frames and some applications
Nicolae Cotfas and Jean Pierre Gazeau · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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Autoencoders, unsupervised learning, and deep architectures
Pierre Baldi · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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Local Eigenvalue Density for General MANOVA Matrices
L. Erdős and B. Farrell · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Learning deep networks from noisy labels with dropout regularization
Ishan Jindal, Matthew S. Nokleby, and Xuewen Chen · 2016
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
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Towards end-to-end speech recognition with deep convolutional neural networks
Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, and Aaron C Courville · 2016
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https://github.com/pat-coady/tiny_imagenet
VGG code for tiny imagenet, 2017 · 2017
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Random subsets of structured deterministic frames have MANOVA spectra
Marina Haikin, Ram Zamir, and Matan Gavish · 2017
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Dropout training as adaptive regularization
Stefan Wager, Sida Wang, and Percy S Liang · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus · 2013
Cited alongside, same era.
Fast dropout training
Sida Wang and Christopher Manning · 2013
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Cited alongside, same era.
The dropout learning algorithm
P. Sadowski P. Baldi · 2014
Cited alongside, same era.
Altitude training: Strong bounds for single-layer dropout
Stefan Wager, William Fithian, Sida Wang, and Percy S Liang · 2014
Cited alongside, same era.
Kazuyuki Hara, Daisuke Saitoh, and Hayaru Shouno · 2017
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Regularization for deep learning: A taxonomy
J. Kukačka, V. Golkov, and D. Cremers · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
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Convolutional neural networks analyzed via convolutional sparse coding
V. Papyan, Y. Romano, and M. Elad · 2017
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Group sparse regularization for deep neural networks
Simone Scardapane, Danilo Comminiello, Amir Hussain, and Aurelio Uncini · 2017
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Robust large margin deep neural networks
J. Sokolic, R. Giryes, G. Sapiro, and M. R. D. Rodrigues · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Information dropout: Learning optimal representations through noisy computation
A. Achille and S. Soatto · 2018
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Dropout as a low-rank regularizer for matrix factorization
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino, and Rene Vidal · 2018
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Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning
Noah Frazier-Logue and Stephen José Hanson · 2018
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Deep metric learning with hierarchical triplet loss
Weifeng Ge, Weilin Huang, Dengke Dong, and Matthew R. Scott · 2018
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Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V. Le · 2018
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Frame moments and welch bound with erasures
Marina Haikin, Ram Zamir, and Matan Gavish · 2018
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On the implicit bias of dropout
Poorya Mianjy, Raman Arora, and Rene Vidal · 2018
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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, and M. Elad · 2018
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Deep learning for computer vision: A brief review
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis · 2018
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On the regularization properties of structured dropout
Ambar Pal, Connor Lane, René Vidal, and Benjamin D. Haeffele · 2020
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Beyond dropout: Feature map distortion to regularize deep neural networks
Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, and Chang Xu · 2020
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