
Combinatorial optimization with reinforcement learning - higgsfield
https://github.com/higgsfield/np-hard-deep-reinforcement-learning
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2bitencryption
I've always wondered how deep learning handles problems with well-understood
time complexities.

How well does the idea of "time complexity" apply to a NN? Since we aren't
performing a series of operations with a NN, we are just passing values
through a mesh of neurons, right?

Would we ever know if a NN "found" a way to solve a certain problem in a more
efficient time complexity than what we currently understood? Can the "time
complexity" concept evne apply to a NN?

~~~
jmite
Can't a neural network only ever achieve an O(1) time approximation of an NP
hard problem? Unless you're using it as a heuristic inside some broader search
algorithm.

