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Large Associative Memory Problem in Neurobiology and Machine Learning
We show that these models are effective descriptions of a more microscopic (written in terms of biological degrees of freedom) theory that has additional (hidden) neurons and only requires two-body interactions between them.
Local Unsupervised Learning for Image Analysis
Local Hebbian learning is believed to be inferior in performance to end-to-end training using a backpropagation algorithm.
Unsupervised Learning by Competing Hidden Units
It is widely believed that the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network.
