Can you mention any key papers about NNs that manipulate existing code?
e.g. parse it, find design pattern, optimize, etc
I think this is just as interesting and already feasible.
Thank you! I found a great list of deep learning paper summaries that is mostly about learning algorithms from data and other cutting edge models: http://colinraffel.com/wiki/tag:neural_networks?do=showtag&t... all kudos to the owner/author of this list !
>NNs that manipulate existing code? e.g. parse it, find design pattern, optimize
Also: you probably shouldn't need special architectures for such problems, these can be solved by seq2seq, NTM, RL-NTM and such, given a good dataset. Such tasks are still beyond current state of art though, it seems.
>NNs that manipulate existing code? e.g. parse it, find design pattern, optimize
Also: you probably shouldn't need special architectures for such problems, these can be solved by seq2seq, NTM, RL-NTM and such, given a good dataset. Such tasks are still beyond current state of art though, it seems.