Optimization of Entropy with Neural Networks
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[1] G. E. Peterson,et al. Control Methods Used in a Study of the Vowels , 1951 .
[2] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[3] Andrei N. Kolmogorov,et al. Logical basis for information theory and probability theory , 1968, IEEE Trans. Inf. Theory.
[4] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[5] John W. Tukey,et al. A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.
[6] Peter E. Hart,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[7] T. Sejnowski,et al. Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.
[8] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[9] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[10] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[11] D. Freedman,et al. Asymptotics of Graphical Projection Pursuit , 1984 .
[12] E. Oja,et al. On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .
[13] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[14] M. F.,et al. Bibliography , 1985, Experimental Gerontology.
[15] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[16] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[17] Robin Sibson,et al. What is projection pursuit , 1987 .
[18] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[19] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[20] James A. Anderson,et al. Neurocomputing: Foundations of Research , 1988 .
[21] Paul W. Munro,et al. Principal Components Analysis Of Images Via Back Propagation , 1988, Other Conferences.
[22] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[23] P. Foldiak,et al. Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.
[24] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[25] Y. J. Tejwani,et al. Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.
[26] Steven J. Nowlan,et al. Maximum Likelihood Competitive Learning , 1989, NIPS.
[27] Y. Le Cun,et al. Comparing different neural network architectures for classifying handwritten digits , 1989, International 1989 Joint Conference on Neural Networks.
[28] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[29] Peter Földiák,et al. Adaptation and decorrelation in the cortex , 1989 .
[30] John S. Bridle,et al. Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters , 1989, NIPS.
[31] F. A. Seiler,et al. Numerical Recipes in C: The Art of Scientific Computing , 1989 .
[32] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[33] Nathan Intrator,et al. A Neural Network for Feature Extraction , 1989, NIPS.
[34] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[35] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[36] Geoffrey E. Hinton,et al. Discovering Viewpoint-Invariant Relationships That Characterize Objects , 1990, NIPS.
[37] Nathan Intrator. Exploratory Feature Extraction in Speech Signals , 1990, NIPS.
[38] Terrence J. Sejnowski,et al. Competitive Anti-Hebbian Learning of Invariants , 1991, NIPS.
[39] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[40] Graeme Mitchison,et al. Removing Time Variation with the Anti-Hebbian Differential Synapse , 1991, Neural Computation.
[41] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[42] T. Leen. Dynamics of learning in linear feature-discovery networks , 1991 .
[43] S. Kung,et al. Neural networks for extracting unsymmetric principal components , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.
[44] Nathan Intrator,et al. Feature Extraction Using an Unsupervised Neural Network , 1992, Neural Computation.
[45] Terrence J. Sejnowski,et al. Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain , 1992, NIPS.
[46] Geoffrey E. Hinton,et al. Self-organizing neural network that discovers surfaces in random-dot stereograms , 1992, Nature.
[47] Paul W. Munro,et al. Visualizations of 2-D hidden unit space , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[48] J. Urgen Schmidhuber,et al. Learning Factorial Codes by Predictability Minimization , 1992, Neural Computation.
[49] Roberto Battiti,et al. First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.
[50] Martin Fodslette Møller,et al. Supervised Learning On Large Redundant Training Sets , 1993, Int. J. Neural Syst..
[51] Lokendra Shastri,et al. Recognizing Handprinted Digit Strings: a Hybrid Connectionist/Procedural Approach , 1993 .
[52] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[53] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .
[54] Terrence J. Sejnowski,et al. Plasticity-Mediated Competitive Learning , 1994, NIPS.
[55] R. Palmer,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[56] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[57] R. Zemel. A minimum description length framework for unsupervised learning , 1994 .
[58] Terrence J. Sejnowski,et al. A Non-linear Information Maximisation Algorithm that Performs Blind Separation , 1994, NIPS.
[59] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[60] Michael A. Arbib,et al. The handbook of brain theory and neural networks , 1995, A Bradford book.
[61] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.