Neural Networks, Types, and Functional Programming (2015)
colah.github.io
Neural Networks, Types, and Functional Programming (2015)
1–10 of 13 posts
Re: Neural Networks, Types, and Functional Programming (2015)
#2Discussed at the time: https://news.ycombinator.com/item?id=10165716.
Re: Neural Networks, Types, and Functional Programming (2015)
#3Note that the article has a comment by Yann LeCun (hopefully it's not an impersonator).
You may also be interested in Differentiable Neural Computers:
- https://deepmind.com/blog/differentiable-neural-computers/
Re: Neural Networks, Types, and Functional Programming (2015)
#4Folks who are into this may also like the "Type Safe Neural Networks in Haskell" https://blog.jle.im/entry/practical-dependent-types-in-haske...
I'm also starting work on a set of bindings to libdarknet for Idris with similar properties.
Re: Neural Networks, Types, and Functional Programming (2015)
#5This covers some of same topics plus the rapidly expanding #s of frameworks, SIMD /SIMT backends etc https://julialang.org/blog/2017/12/ml&pl
20 pg tutorial on why RNN's are tricky https://arxiv.org/abs/1801.01078
Re: Neural Networks, Types, and Functional Programming (2015)
#6This covers some of same topics plus the rapidly expanding #s of frameworks, SIMD /SIMT backends etc https://julialang.org/blog/2017/12/ml&pl 20 pg tutorial on why RNN's are tricky https://arxiv.org/abs/1801.01078
Many mentions of Python, none of Numba, which does a good job of jitting Python with LLVM.
It seems unlikely though that an entire modeling community could rally behind a single language or framework, given all the possibilities, many of which are commercially oriented. But one I’ve used recently with a lot of flexibility is Loopy
Re: Neural Networks, Types, and Functional Programming (2015)
#7Note that the article has a comment by Yann LeCun (hopefully it's not an impersonator). You may also be interested in Differentiable Neural Computers: - https://deepmind.com/blog/differentiable-neural-computers/ - https://github.com/deepmind/dnc
> Note that the article has a comment by Yann LeCun (hopefully it's not an impersonator).
i wouldnt be surprised if it was lecun. colah's illustrations on nonlinear transformations have made it into several lecun papers, including the following [nature review]( https://www.nature.com/articles/nature14539).
Re: Neural Networks, Types, and Functional Programming (2015)
#8Some related work also include:
- "Strongly-Typed Recurrent Neural Networks" http://proceedings.mlr.press/v48/balduzzi16.pdf
- Principled Approaches to Deep Learning workshop http://padl.ws/ (maybe this is in line with the meta-point Colah's paper)
- Haskell accelerate library https://github.com/AccelerateHS/accelerate/. Not deep learning per se but perhaps some of the ideas are applicable
Re: Neural Networks, Types, and Functional Programming (2015)
#9The examples are odd because he doesn't incorporate any notion of differentiability.
So a Generating RNN is not quite like foldr, since foldr has no notion of differentiability.
One needs to show examples that pulls in some kind of automatic-differentiation capability.
Re: Neural Networks, Types, and Functional Programming (2015)
#10The examples are odd because he doesn't incorporate any notion of differentiability. So a Generating RNN is not quite like foldr, since foldr has no notion of differentiability. One needs to show examples that pulls in some kind of automatic-differentiation capability.
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