Fetching the paper…
Reading the bibliography…
With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence.
Possible principles underlying the transformations of sensory messages
Horace B Barlow · 1961
Earlier work this paper cites.
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Joseph B Kruskal · 1964
Earlier work this paper cites.
Space or time adaptive signal processing by neural network models
Jeanny Herault and Christian Jutten · 1986
Earlier work this paper cites.
Finding Minimum Entropy Codes
H.B. Barlow, T.P. Kaushal, and G.J. Mitchison · 1989
Earlier work this paper cites.
Unsupervised Learning
Horrace Barlow · 1989
Earlier work this paper cites.
Forming sparse representations by local anti-Hebbian learning
P. Foldiak · 1990
Earlier work this paper cites.
Tensor product variable binding and the representation of symbolic structures in connectionist systems
Paul Smolensky · 1990
Earlier work this paper cites.
Unsupervised learning procedures for neural networks
Suzanna Becker · 1991
Earlier work this paper cites.
Learning Factorial Codes by Predictability Minimization
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
A minimum description length framework for unsupervised learning
Richard S Zemel · 1993
Earlier work this paper cites.
A Minimum Description Length Framework for Unsupervised Learning
Richard Stanley Zemel · 1993
Earlier work this paper cites.
Functional parts
J Tenenbaum · 1994
Earlier work this paper cites.
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Anthony J. Bell and Terrence J. Sejnowski · 1995
Earlier work this paper cites.
Factorial Learning and the EM Algorithm
Zoubin Ghahramani, G Tesauro, D S Touretzky, and T K Leen · 1995
Earlier work this paper cites.
A Multiple Cause Mixture Model for Unsupervised Learning
Eric Saund · 1995
Earlier work this paper cites.
Factorial Learning by Clustering Features
Joshua B Tenenbaum and Emanuel Todorov · 1995
Earlier work this paper cites.
Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map
Teuvo Kohonen · 1996
Earlier work this paper cites.
Adaptive blind signal processing-neural network approaches
Shun-ichi Amari and Andrzej Cichocki · 1998
Earlier work this paper cites.
Fast and robust fixed-point algorithms for independent component analysis
Aapo Hyvarinen · 1999
Earlier work this paper cites.
Learning the parts of objects by non-negative matrix factorization
D D Lee and H S Seung · 1999
Earlier work this paper cites.
Source separation in post-nonlinear mixtures
Anisse Taleb and Christian Jutten · 1999
Earlier work this paper cites.
Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces
Aapo Hyvärinen and Patrik Hoyer · 2000
Earlier work this paper cites.
Separating style and content with bilinear models
J B Tenenbaum and W T Freeman · 2000
Cited alongside, same era.
Feature extraction using supervised independent component analysis by maximizing class distance
Yoshinori Sakaguchi, Seiichi Ozawa, and Manabu Kotani · 2002
Cited alongside, same era.
Learning higher-order structures in natural images
Yan Karklin and Michael S Lewicki · 2003
Cited alongside, same era.
Multiple Cause Vector Quantization
David A Ross and Richard S Zemel · 2003
Cited alongside, same era.
Learning a similiarty metric discriminatively, with application to face verification
S Chopra, R Hadsell, and LeCun Y · 2005
Cited alongside, same era.
Bilinear Sparse Coding for Invariant Vision
David B Grimes · 2005
Cited alongside, same era.
Visualizing non-metric similarities in multiple maps
Laurens Van Der Maaten and Geoffrey Hinton · 2012
Later among the works it cites.
Analogy-preserving Semantic Embedding for Visual Object Categorization
Sung Ju Hwang, Kristen Grauman, and Fei Sha · 2013
Later among the works it cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
Later among the works it cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Later among the works it cites.
Tensor Analyzers
Yichuan Tang and Geoffrey Hinton · 2013
Later among the works it cites.
Discovering Hidden Factors of Variation in Deep Networks
Brian Cheung, Jesse a. Livezey, Arjun K. Bansal, and Bruno a. Olshausen · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pattern recognition
Christopher M Bishop · 2006
Cited alongside, same era.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Cited alongside, same era.
Recursive ICA
Honghao Shan, Lingyun Zhang, and Garrison W Cottrell · 2006
Cited alongside, same era.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Cited alongside, same era.
Extracting and Composing Robust Features with Denoising Autoencoders
Pascal Vincent and Hugo Larochelle · 2008
Cited alongside, same era.
The effect of distributional information on feature learning
Jl Austerweil and Tl Griffiths · 2009
Cited alongside, same era.
Later among the works it cites.
Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc Aurelio Ranzato, and Ilya Sutskever · 2014
Later among the works it cites.
Semi-supervised Learning with Deep Generative Models
Dp Kingma, Dj Rezende, and Max Welling · 2014
Later among the works it cites.
Learning to Disentangle Factors of Variation with Manifold Interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
Later among the works it cites.
Learning fine-grained image similarity with deep ranking
Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu · 2014
Later among the works it cites.
Multi-View Perceptron : a Deep Model for Learning Face Identity and View Representations
Zhenyao Zhu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2014
Later among the works it cites.
Multiview Triplet Embedding : Learning Attributes in Multiple Maps
Ehsan Amid and Antti Ukkonen · 2015
Later among the works it cites.
Bayesian representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, and Gunnar Rätsch · 2015
Later among the works it cites.
Deep Convolutional Inverse Graphics Network
TD Kulkarni and W Whitney · 2015
Later among the works it cites.
Winner-Take-All Autoencoders
Alireza Makhzani and Brendan J Frey · 2015
Later among the works it cites.
Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
Later among the works it cites.
Deep Visual Analogy-Making
Scott E. Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
Later among the works it cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Later among the works it cites.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Jimei Yang, Ming-Hsuan Yang, Scott E. Reed, and Honglak Lee · 2015
Later among the works it cites.
Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks
Andreas Veit, Serge Belongie, and Theofanis Karaletsos · 2016
Closest in time.
Joint Learning of Speaker and Phonetic Similarities with Siamese Networks
Neil Zeghidour, Gabriel Synnaeve, Nicolas Usunier, and Emmanuel Dupoux · 2016
Closest in time.