Artificial Intelligence
Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
G-maximization: An unsupervised learning procedure for discovering regularities
AIP Conference Proceedings 151 on Neural Networks for Computing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Adaptation and decorrelation in the cortex
The computing neuron
Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
An Investigation of the Laws of Thought
An Investigation of the Laws of Thought
Neural Computation
VideoCube: A Novel Tool for Video Mining and Classification
ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
Towards a New Information Processing Measure for Neural Computation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Unsupervised Learning of Visual Structure
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Factorial Code Representation of Faces for Recognition
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Information maximization in face processing
Neurocomputing
Neural Computation
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
A refinement of the common cause principle
Discrete Applied Mathematics
Linguistics and face recognition
Journal of Visual Languages and Computing
A view-based statistical system for multi-view face detection and pose estimation
Image and Vision Computing
Salience in orientation-filter response measured as suspicious coincidence in natural images
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A motor learning neural model based on Bayesian network and reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
(2D)2PCA-ICA: a new approach for face representation and recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multiple self-splitting and merging competitive learning algorithm
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Text extraction from graphical document images using sparse representation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Towards a noisy-channel model of dysarthria in speech recognition
SLPAT '10 Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies
Local non-linear interactions in the visual cortex may reflect global decorrelation
Journal of Computational Neuroscience
Learning image transformations without training examples
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Locally adaptive nonlinear dimensionality reduction
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Self-organizing isometric embedding based on statistical criterions
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Attractor memory with self-organizing input
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Improved self-splitting competitive learning algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Low frequency response and random feature selection applied to face recognition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Dynamics of feature categorization
Neural Computation
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What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for understanding perception (Attneave 1954), the physiology of sensory pathways (Barlow 1959), and pattern recognition (Watanabe 1960). It is argued that (1) The redundancy of sensory messages provides the knowledge incorporated in the maps or models. (2) Some of this knowledge can be obtained by observations of mean, variance, and covariance of sensory messages, and perhaps also by a method called minimum entropy coding. (3) Such knowledge may be incorporated in a model of what usually happens with which incoming messages are automatically compared, enabling unexpected discrepancies to be immediately identified. (4) Knowledge of the sort incorporated into such a filter is a necessary prerequisite of ordinary learning, and a representation whose elements are independent makes it possible to form associations with logical functions of the elements, not just with the elements themselves.