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by robert_tweed
4256 days ago
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Recurrent neural networks work that way, but typically when anyone says "neural network" they mean a feed-forward network, which is as dumb as a lead pipe. It has no memory other than the trained synapse weights. After training, all it does is transform some input data into output data. It's a pure function. It's also a lot like a matrix transform. I haven't read this paper fully yet, but it seems to be an attempt at simplifying RNNs by replacing some of the magic internal state, which tends to make them hard to reason about, with a more direct memory architecture. |
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