S. Hochreiter and J. Schmidhuber. Source separation as a by-product of regularization. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA, 1999. Home/Search Document Details and Download
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Schmidhuber's Lab - Turteltaub (1999) (Correct)
....100 papers on diverse topics including fine arts [96] and the nature of surprises [97] Apparently he even founded a religion [94] Most of his articles, however, are about machines that learn from experience. I have started to compile an incomplete list of references to work by him and his lab [117, 116, 39, 50, 40, 42, 43, 41, 52, 49, 56, 44, 54, 47, 48, 51, 53, 57, 46, 68, 45, 55, 69, 64, 65, 59, 66, 58, 67, 60, 63, 61, 73, 71, 79, 70, 74, 62, 72, 75, 78, 82, 80, 76, 81, 77, 84, 89, 88, 94, 87, 85, 96, 83, 100, 86, 90, 99, 91, 93, 105, 119, 95, 92, 97, 120, 118, 98, 125, 130, 129, 126, 128, 124, 123, 122, 131, 127, 35, 34, 36, 38, 32, 33, 37, 27, 28, 25, 24, 22, 23, 15, 9, 21, 10, 16, 26, 17, 18, 6, 7, 8, 13, 11, 20, 19, 14, 12, 115, 114, 121, 30, 106, 108, 107, 29, 31, 109, 110, 111, 112, 113, 5, 101, 103, 104, 4, 3, 2, 1, 102]. Hopefully I ll be able to add missing entries soon. Future work will concentrate on categorizing related papers and establishing common threads. ....
S. Hochreiter and J. Schmidhuber. Source separation as a by-product of regularization. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA, 1999.
Nonlinear Ica Through Low-Complexity Autoencoders - Hochreiter, Schmidhuber Self-citation (Hochreiter Schmidhuber) (Correct)
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S. Hochreiter and J. Schmidhuber. Source separation as a by-product of regularization. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA, 1999.
The Work of Schmidhuber 1987-2002 - Hufnagel (2002) Self-citation (Schmidhuber) (Correct)
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S. Hochreiter and J. Schmidhuber. Source separation as a by-product of regularization. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12, pages 459-465. MIT Press, 1999.
Unsupervised Learning in Recurrent Neural Networks - Klapper-Rybicka.. Self-citation (Schmidhuber) (Correct)
.... of encoding input sequences through RNNs in a nonredundant fashion [35, 36] b) unsupervised RNN based compression of input sequences to facilitate subsequent processing by another RNN [37 39] and (c) an illustrative toy application of the recent redundancy minimization technique lococode [31] to sequence processing RNNs [30] One of the reasons for the conventional focus on FFNs may be the relative maturity of this architecture, and the algorithms used to train it. Compared to FFNs, traditional RNNs [41 43] are notoriously dicult to train, especially when the interval between ....
S. Hochreiter and J. Schmidhuber, \Source separation as a by-product of regularization, " in Advances in Neural Information Processing Systems 12 (M. Kearns, S. A. Solla, and D. Cohn, eds.), pp. 459-465, MIT Press, Cambridge MA, 1999.
Unsupervised Learning in Recurrent Neural Networks - Klapper-Rybicka.. (2000) Self-citation (Schmidhuber) (Correct)
.... of encoding input sequences through RNNs in a nonredundant fashion [35, 36] b) unsupervised RNN based compression of input sequences to facilitate subsequent processing by another RNN [37 39] and (c) an illustrative toy application of the recent redundancy minimization technique lococode [31] to sequence processing RNNs [30] One of the reasons for the conventional focus on FFNs may be the relative maturity of this architecture, and the algorithms used to train it. Compared to FNNs, traditional RNNs [40 42] are notoriously dicult to train, especially when the interval between ....
S. Hochreiter and J. Schmidhuber, \Source separation as a by-product of regularization, " in Advances in Neural Information Processing Systems 12 (M. Kearns, S. A. Solla, and D. Cohn, eds.), pp. 459-465, MIT Press, Cambridge MA, 1999.
Nonlinear ICA Through Low-Complexity Autoencoders - Sepp Hochreiter, Jürgen.. Self-citation (Hochreiter Schmidhuber) (Correct)
....output unit (this leads to separation of CFs) while simultaneously (2) using the same CFs for as many output units as possible (common CFs) The results above give rise to a new method for source separation: simply train autoencoders (e.g. 2, 3, 4, 5] via FMS. The method s name is Lococode [6, 7, 8, 9, 10], which stands for Low complexity coding and decoding. Lococode generates lococodes that (1) convey information about the input data, 2) can be computed by a low complexity mapping (LCM) 3) can be decoded by a LCM (for alternative approaches using low complexity nets to achieve ICA see [11, ....
.... 27] inherently limited to the linear case [10] b) does not need (like ICA) a priori information about the number of independent data sources (even when ICA knows the number of sources, Lococode outperforms ICA) 8] and (c) has a higher coding efficiency (bits per input pixel) than PCA and ICA [9]. Unlike codes obtained with standard autoencoders, lococodes are based on feature detectors, never unstructured, usually sparse, sometimes factorial or local (depending on statistical properties of the data) Although Lococode is not explicitly designed to enforce sparse or factorial codes, it ....
S. Hochreiter and J. Schmidhuber. Source separation as a by-product of regularization. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA, 1999.
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