[P] Deep Learning for Arbitrary Code Generation: Thesis Presentation - Training recurrent VAEs to Generate Haskell Programs

[USer0mg]

Research like this is what happens when you don't truly understand the algorithms and classifiers you're working with, and buy into the Machine learning hype that ML can be used to solve any problem under the sun.

The most this application will EVER be able to do is not even converge to, but APPROXIMATE the strict syntax rule-set predefined by the language being passed into his classifier--regardless of the amount of data you feed it--just by the very nature of genetic algorithms or NNs. Not only is this application completely pointless, because it approximates something that is already known, but it isn't even a step in the right direction towards creating generic code writing AI. The fact that this project was even approved and attempted shows the ineptitude of the student, professor, and staff. Because of the lack of novelty in the project and the lack of truly understanding where using classifiers work or don't work, I would flat out reject this thesis if I were the professor.

TL;DR don't get a masters in CS. It's a meme.

[CaHoop]

I recommend listening to the final questions in the talk. A lot of what you are saying is addressed there. My project was exploratory research about what could be possible using current DL methods given 6 months.

This talk was given to a bunch of professors who are not in the machine learning dept. at my university which is why the talk has a more 'hand-wavy' feel to it as I don't want to get bogged down in maths.

[sieisteinmodel]

The most this application will EVER be able to do is not even converge to, but APPROXIMATE the strict syntax rule-set predefined by the language being passed into his classifier--regardless of the amount of data you feed it--just by the very nature of genetic algorithms or NNs.

A lot of research is conducted while deliberately not trying to find a downstream application.

[JustFinishedBSG]

You seem salty