Conditioning, Mutual Information, and Information Gain
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[1] Geoffrey E. Hinton,et al. Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[2] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[3] Robert A. Meyers,et al. Encyclopedia of Complexity and Systems Science , 2009 .
[4] Joseph J Atick,et al. Could information theory provide an ecological theory of sensory processing? , 2011, Network.
[5] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[6] H. B. Barlow,et al. Unsupervised Learning , 1989, Neural Computation.
[7] E. Jaynes. Information Theory and Statistical Mechanics , 1957 .
[8] Gasper Tkacik,et al. Cell biology: Networks, regulation, pathways , 2007, 0712.4385.
[9] Roberto Battiti,et al. Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.
[10] Gavin Brown,et al. A New Perspective for Information Theoretic Feature Selection , 2009, AISTATS.
[11] G. Deco,et al. An Information-Theoretic Approach to Neural Computing , 1997, Perspectives in Neural Computing.
[12] Ralph Linsker,et al. Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.
[13] H Herzel,et al. Information content of protein sequences. , 2000, Journal of theoretical biology.
[14] Donall A. Mac Donaill. Molecular informatics: Hydrogen-bonding, error-coding, and genetic replication , 2009, CISS 2009.
[15] Ebeling,et al. Entropies of biosequences: The role of repeats. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[16] Friedrich T. Sommer,et al. Adaptive compressed sensing — A new class of self-organizing coding models for neuroscience , 2009, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[17] Barak A. Pearlmutter,et al. G-maximization: An unsupervised learning procedure for discovering regularities , 1987 .
[18] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[19] William M. Campbell,et al. Mutual Information in Learning Feature Transformations , 2000, ICML.
[20] Ryotaro Kamimura. Information theoretic neural computation , 2002 .
[21] S. Buldyrev,et al. Species independence of mutual information in coding and noncoding DNA. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[22] William Bialek,et al. Cell Biology: Networks, Regulation and Pathways , 2009, Encyclopedia of Complexity and Systems Science.
[23] Andrei N. Kolmogorov,et al. On the Shannon theory of information transmission in the case of continuous signals , 1956, IRE Trans. Inf. Theory.
[24] Deniz Erdogmus,et al. Information Theoretic Learning , 2005, Encyclopedia of Artificial Intelligence.
[25] Gianluigi Mongillo,et al. Online Learning with Hidden Markov Models , 2008, Neural Computation.
[26] T. Nayak. Statistical Significance: Rationale, Validity and Utility , 1997 .
[27] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[28] Deniz Erdoğmuş,et al. Online entropy manipulation: stochastic information gradient , 2003, IEEE Signal Processing Letters.
[29] S. Amari. Differential Geometry of Curved Exponential Families-Curvatures and Information Loss , 1982 .
[30] William Bialek,et al. Estimating mutual information and multi-information in large networks , 2005, ArXiv.
[31] John S Denker,et al. AIP Conference Proceedings 151 on Neural Networks for Computing , 1987 .
[32] Ralph Linsker,et al. How to Generate Ordered Maps by Maximizing the Mutual Information between Input and Output Signals , 1989, Neural Computation.
[33] Robert Jenssen,et al. Spectral feature projections that maximize Shannon mutual information with class labels , 2006, Pattern Recognit..
[34] H. Bauer,et al. Probability Theory and Elements of Measure Theory , 1982 .
[35] S. F. Taylor,et al. Information and fitness , 2007, 0712.4382.
[36] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[37] Geoffrey E. Hinton,et al. Learning Population Codes by Minimizing Description Length , 1993, Neural Computation.
[38] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[39] Jonathan A. Marshall,et al. An introduction to neural and electronic networks: Edited by Steven F. Zornetzer, Joel L. Davis, and Clifford Lau, Academic Press, San Diego, CA: 1990, hardcover $99.50, paperback $44.95, 493 pp., ISBN 0-12-781881-2 , 1992 .
[40] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[41] Ralph Linsker,et al. A Local Learning Rule That Enables Information Maximization for Arbitrary Input Distributions , 1997, Neural Computation.
[42] H. Herzel,et al. Estimating the entropy of DNA sequences. , 1997, Journal of theoretical biology.
[43] Aapo Hyvärinen,et al. An alternative approach to infomax and independent component analysis , 2002, Neurocomputing.
[44] Shun-ichi Amari,et al. A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..
[45] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[46] E. Jaynes. On the rationale of maximum-entropy methods , 1982, Proceedings of the IEEE.