RandomIsAMyth t1_j2gcm0b wrote
>Stripping away the neural network and running the underlying algorithm could be useful, since classical algorithms tend to run much faster and with less memory.
It's not clear what you call classical algorithm here and I wonder how you would find such algorithm inside a neural network.
The entire neural network is the algorithm. Deleting/changing any parameter could damage the network accuracy. Also, the most costly operations are matrix multiplications but you can hardly speed up multiplications and additions in today's computers. Making the matrix multiplication simpler using quantization and/or sparsity is probably the way to go.
currentscurrents OP t1_j2gdumb wrote
By classical algorithm, I mean something that doesn't use a neural network. Traditional programming and neural networks are two very different ways to solve problems, but they can solve many of the same problems.
That sounds like a translation problem, which neural networks are good at. Just like in translation, it would have to understand the higher-level idea behind the implementation.
It's like text-to-code, but network-to-code instead.
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