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A simple probabilistic neural network for machine understanding
We discuss probabilistic neural networks with a fixed internal representation as models for machine understanding.
A random energy approach to deep learning
We study a generic ensemble of deep belief networks which is parametrized by the distribution of energy levels of the hidden states of each layer.
Active image restoration
Within this regime the strategy is outperformed by its local (adaptive) version, which supervises pixels that do not agree with their Bayesian estimate.
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