Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
What does the retina know about natural scenes?
Neural Computation
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Selecting weighting factors in logarithmic opinion pools
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Training products of experts by minimizing contrastive divergence
Neural Computation
Discovering Multiple Constraints that are Frequently Approximately Satisfied
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Energy-based models for sparse overcomplete representations
The Journal of Machine Learning Research
Topographic Independent Component Analysis
Neural Computation
Learning Overcomplete Representations
Neural Computation
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Probabilistic sequential independent components analysis
IEEE Transactions on Neural Networks
A Maximum-Likelihood Interpretation for Slow Feature Analysis
Neural Computation
A dynamical model for receptive field self-organization in V1 cortical columns
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A two-layer ICA-like model estimated by score matching
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A two-layer model of natural stimuli estimated with score matching
Neural Computation
Unsupervised learning of hierarchical representations with convolutional deep belief networks
Communications of the ACM
Learning Topographic Representations of Nature Images with Pairwise Cumulant
Neural Processing Letters
The Journal of Machine Learning Research
Learning invariant feature hierarchies
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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We present an energy-based model that uses a product of generalized Student-t distributions to capture the statistical structure in data sets. This model is inspired by and particularly applicable to "natural" data sets such as images. We begin by providing the mathematical framework, where we discuss complete and overcomplete models and provide algorithms for training these models from data. Using patches of natural scenes, we demonstrate that our approach represents a viable alternative to independent component analysis as an interpretive model of biological visual systems. Although the two approaches are similar in flavor, there are also important differences, particularly when the representations are overcomplete. By constraining the interactions within our model, we are also able to study the topographic organization of Gabor-like receptive fields that our model learns. Finally, we discuss the relation of our new approach to previous work—in particular, gaussian scale mixture models and variants of independent components analysis.