Fetching the paper…
Reading the bibliography…
Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous discovery of discriminating features during training.
Rumelhart, D.E., Zipser, D.: Feature discovery by competitive learning. Cognitive science 9(1), 75–112 (1985)
1985
Earlier work this paper cites.
Oja, E.: Neural networks, principal components, and subspaces. International journal of neural systems 1(01), 61–68 (1989)
1989
Earlier work this paper cites.
Sanger, T.D.: Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural networks 2(6), 459–473 (1989)
1989
Earlier work this paper cites.
Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)
1996
Earlier work this paper cites.
Cox, T.F., Cox, M.A.: Multidimensional scaling. CRC press (2000)
2000
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
2009
Cited alongside, same era.
Krizhevsky, A., Hinton, G.: Convolutional deep belief networks on cifar-10. Unpublished manuscript 40 (2010)
2010
Cited alongside, same era.
Coates, A., Lee, H., Ng, A.Y.: An analysis of single-layer networks in unsupervised feature learning. In: AISTATS 2011. vol. 1001 (2011)
2011
Cited alongside, same era.
Sohn, K., Lee, H.: Learning invariant representations with local transformations. In: Proceedings of the 29th International Conference on Machine Learning (ICML-12). pp. 1311–1318 (2012)
2012
Cited alongside, same era.
Lin, T.h., Kung, H.: Stable and efficient representation learning with nonnegativity constraints. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14). pp. 1323–1331 (2014)
2014
Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: Convolutional kernel networks. In: Advances in Neural Information Processing Systems. pp. 2627–2635 (2014)
2014
Later among the works it cites.
Pehlevan, C., Chklovskii, D.B.: A Hebbian/anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features. In: 2014 48th Asilomar Conference on Signals, Systems and Computers. pp. 769–775. IEEE (2014)
2014
Later among the works it cites.
Pehlevan, C., Chklovskii, D.: A normative theory of adaptive dimensionality reduction in neural networks. In: Advances in Neural Information Processing Systems. pp. 2269–2277 (2015)
2015
Later among the works it cites.
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Poikonen, J.H., Laiho, M.: Online linear subspace learning in an analog array computing architecture. CNNA 2016 (2016)
2016
Later among the works it cites.