Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Integrated segmentation and recognition of hand-printed numerals
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Learning factorial codes by predictability minimization
Neural Computation
A multiple cause mixture model for unsupervised learning
Neural Computation
Neural Computation
Filter Selection Model for Generating Visual Motion Signals
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
Adaptive mixtures of local experts
Neural Computation
Neural Computation
A Neural Network for PCA and Beyond
Neural Processing Letters
Variational learning in nonlinear Gaussian belief networks
Neural Computation
Recurrent sampling models for the Helmholtz machine
Neural Computation
Feature extraction through LOCOCODE
Neural Computation
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Optimal Extraction of Hidden Causes
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Topic Extraction from Text Documents Using Multiple-Cause Networks
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Learning with mixtures of trees
The Journal of Machine Learning Research
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Building Blocks for Variational Bayesian Learning of Latent Variable Models
The Journal of Machine Learning Research
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Generalized softmax networks for non-linear component extraction
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A nonnegative blind source separation model for binary test data
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Expectation Truncation and the Benefits of Preselection In Training Generative Models
The Journal of Machine Learning Research
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If different causes can interact on any occasion to generate a set of patterns, then systems modeling the generation have to model the interaction too. We discuss a way of combining multiple causes that is based on the Integrated Segmentation and Recognition architecture of Keeler et al. (1991). It is more cooperative than the scheme embodied in the mixture of experts architecture, which insists that just one cause generate each output, and more competitive than the noisy-or combination function, which was recently suggested by Saund (1994a,b). Simulations confirm its efficacy.