Fetching the paper…
Reading the bibliography…
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Multivariate information transmission
McGill, William J · 1954
Earlier work this paper cites.
The hungarian method for the assignment problem
Kuhn, Harold W · 1955
Earlier work this paper cites.
Unsupervised classifiers, mutual information and ’phantom targets’
Bridle, John S., Heading, Anthony J. R., and MacKay, David J. C · 1991
Earlier work this paper cites.
Newsweeder: Learning to filter netnews
Lang, Ken · 1995
Earlier work this paper cites.
From data distributions to regularization in invariant learning
Leen, Todd K · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
Earlier work this paper cites.
Nonlinear programming
Bertsekas, Dimitri P · 1999
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Ng, Andrew Y, Jordan, Michael I, Weiss, Yair, et al · 2001
Earlier work this paper cites.
Modeling the shape of the scene: A holistic representation of the spatial envelope
Oliva, Aude and Torralba, Antonio · 2001
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Grandvalet, Yves, Bengio, Yoshua, et al · 2004
Earlier work this paper cites.
Rcv1: A new benchmark collection for text categorization research
Lewis, David D, Yang, Yiming, Rose, Tony G, and Li, Fan · 2004
Earlier work this paper cites.
Maximum margin clustering
Xu, Linli, Neufeld, James, Larson, Bryce, and Schuurmans, Dale · 2004
Earlier work this paper cites.
Self-tuning spectral clustering
Zelnik-Manor, Lihi and Perona, Pietro · 2004
Earlier work this paper cites.
A survey of clustering data mining techniques
Berkhin, Pavel · 2006
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, Antonio, Fergus, Rob, and Freeman, William T · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Vincent, Pascal, Larochelle, Hugo, Bengio, Yoshua, and Manzagol, Pierre-Antoine · 2008
Earlier work this paper cites.
A new perspective for information theoretic feature selection
Brown, Gavin · 2009
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
Jarrett, Kevin, Kavukcuoglu, Koray, Ranzato, Marc’Aurelio, and LeCun, Yann · 2009
Earlier work this paper cites.
Learning to hash with binary reconstructive embeddings
Kulis, Brian and Darrell, Trevor · 2009
Cited alongside, same era.
Semantic hashing
Salakhutdinov, Ruslan and Hinton, Geoffrey · 2009
Cited alongside, same era.
Spectral hashing
Weiss, Yair, Torralba, Antonio, and Fergus, Rob · 2009
Cited alongside, same era.
An analysis of single-layer networks in unsupervised feature learning
Coates, Adam, Lee, Honglak, and Ng, Andrew Y · 2010
Cited alongside, same era.
Discriminative clustering by regularized information maximization
Gomes, Ryan, Krause, Andreas, and Perona, Pietro · 2010
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
Cited alongside, same era.
Supervised hashing for image retrieval via image representation learning
Xia, Rongkai, Pan, Yan, Lai, Hanjiang, Liu, Cong, and Yan, Shuicheng · 2014
Later among the works it cites.
Deep hashing for compact binary codes learning
Erin Liong, Venice, Lu, Jiwen, Wang, Gang, Moulin, Pierre, and Zhou, Jie · 2015
Later among the works it cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Later among the works it cites.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
Later among the works it cites.
Siamese neural networks for one-shot image recognition
Koch, Gregory · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011
Cited alongside, same era.
One shot learning of simple visual concepts
Lake, Brenden M, Salakhutdinov, Ruslan, Gross, Jason, and Tenenbaum, Joshua B · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
Cited alongside, same era.
Minimal loss hashing for compact binary codes
Norouzi, Mohammad and Blei, David M · 2011
Cited alongside, same era.
Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, Salah, Vincent, Pascal, Muller, Xavier, Glorot, Xavier, and Bengio, Yoshua · 2011
Cited alongside, same era.
Elements of information theory
Cover, Thomas M and Thomas, Joy A · 2012
Cited alongside, same era.
Later among the works it cites.
Simultaneous feature learning and hash coding with deep neural networks
Lai, Hanjiang, Pan, Yan, Liu, Ye, and Yan, Shuicheng · 2015
Later among the works it cites.
Feature learning based deep supervised hashing with pairwise labels
Li, Wu-Jun, Wang, Sheng, and Kang, Wang-Cheng · 2015
Later among the works it cites.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Springenberg, Jost Tobias · 2015
Later among the works it cites.
Chainer: a next-generation open source framework for deep learning
Tokui, Seiya, Oono, Kenta, Hido, Shohei, and Clayton, Justin · 2015
Later among the works it cites.
Convolutional neural networks for text hashing
Xu, Jiaming, Wang, Peng, Tian, Guanhua, Xu, Bo, Zhao, Jun, Wang, Fangyuan, and Hao, Hongwei · 2015
Later among the works it cites.
Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification
Zhang, Ruimao, Lin, Liang, Zhang, Rui, Zuo, Wangmeng, and Zhang, Lei · 2015
Later among the works it cites.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Dilokthanakul, Nat, Mediano, Pedro AM, Garnelo, Marta, Lee, Matthew CH, Salimbeni, Hugh, Arulkumaran, Kai, and Shanahan, Murray · 2016
Later among the works it cites.
Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
Later among the works it cites.
Distributional smoothing with virtual adversarial training
Miyato, Takeru, Maeda, Shin-ichi, Koyama, Masanori, Nakae, Ken, and Ishii, Shin · 2016
Later among the works it cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, Mehdi, Javanmardi, Mehran, and Tasdizen, Tolga · 2016
Later among the works it cites.
Learning to hash for indexing big data—a survey
Wang, Jun, Liu, Wei, Kumar, Sanjiv, and Chang, Shih-Fu · 2016
Later among the works it cites.
Unsupervised deep embedding for clustering analysis
Xie, Junyuan, Girshick, Ross, and Farhadi, Ali · 2016
Later among the works it cites.