3 ms·

I don't think this is the reason. You can do pretty much anything in Torch/Theano. But, researchers are lazy and writing code is only a means to an end. They wi

I don't think this is the reason. You can do pretty much anything in Torch/Theano. But, researchers are lazy and writing code is only a means to an end. They will shamelessly copy/hack as much open source code as possible, so if a framework has some quick module for a desired algorithm, or if a researcher wrote their paper in some framework, and you want to build off that paper, then you'll just copy that.

tedmiston 11y ago
That's definitely correct. One related unsolved problem in that type of research is getting people to share their actual code. Especially when multiple universities are at the bleeding edge in a field, they often publish just enough [1] to prove their point without giving everyone else the same foundation to build on easily. Even in science it gets political... who knew. 1: i.e., just their algorithms, or their code without very useful implementation-level optimizations
- argonaut 11y ago
  In ML, you can generally email the authors and very often they will be willing to send you (their really crappy) code. Although it probably helped that I sent these emails from my academic email address.
  - sgt101 11y ago
    I guess that by the time the paper is in the public domain things have moved on - it's six months from submission to publication.
  - pacala 11y ago
    > There has never been a "need to learn multiple frameworks in multiple languages." > you can generally email the authors and very often they will be willing to send you (their really crappy) code. Code obtained from multiple authors, or even from the same author but different time periods, is code written using multiple frameworks in multiple languages. Standardizing on Python / TensorFlow reduces the risk of cognitive load along one's journey and is likely to speed up the field. If speed is what the field was missing :)

3 ms·

I don't think this is the reason. You can do pretty much anything in Torch/Theano. But, researchers are lazy and writing code is only a means to an end. They wi

by argonaut 11y ago

tedmiston 11y ago
That's definitely correct. One related unsolved problem in that type of research is getting people to share their actual code. Especially when multiple universities are at the bleeding edge in a field, they often publish just enough [1] to prove their point without giving everyone else the same foundation to build on easily. Even in science it gets political... who knew. 1: i.e., just their algorithms, or their code without very useful implementation-level optimizations
- argonaut 11y ago
  In ML, you can generally email the authors and very often they will be willing to send you (their really crappy) code. Although it probably helped that I sent these emails from my academic email address.
  - sgt101 11y ago
    I guess that by the time the paper is in the public domain things have moved on - it's six months from submission to publication.
  - pacala 11y ago
    > There has never been a "need to learn multiple frameworks in multiple languages." > you can generally email the authors and very often they will be willing to send you (their really crappy) code. Code obtained from multiple authors, or even from the same author but different time periods, is code written using multiple frameworks in multiple languages. Standardizing on Python / TensorFlow reduces the risk of cognitive load along one's journey and is likely to speed up the field. If speed is what the field was missing :)