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A Differentiable Programming System to Bridge Machine Learning and Scientific Computing

Comments

Jul 19 2019 michael_nielsen

I hope this is part of a trend exploring more and more in this direction. As @karpathy said, "gradient descent is a better programmer than you": https://arxiv.org/abs/1907.07587 https://t.co/FhxtH9YUe2
6 replies, 429 likes


Jul 18 2019 Chris Rackauckas

#julialang is enabling scientific machine learning. Take at a the paper submitted to NeurIPS, which showcases quantum machine learning and neural stochastic differential equation (neural SDE) as applications. Many talks at #juliacon will explore this idea https://arxiv.org/pdf/1907.07587.pdf
3 replies, 61 likes


Jul 19 2019 Donald Fischer

"Differentiable Programming (∂P) has the potential to be the lingua franca to unite scientific computing and machine learning. [...] Our ∂P system extends @JuliaLanguage with differentiable programming capabilities."
0 replies, 15 likes


Jul 18 2019 Roger Luo 罗秀哲

Zygote paper is out with a VQE demo made with Yao! I'm looking forward to the talk. Nice work @MikeJInnes https://arxiv.org/abs/1907.07587
0 replies, 6 likes


Jul 19 2019 Hacker News

∂P: A Differentiable Programming System to Bridge ML and Scientific Computing https://arxiv.org/abs/1907.07587
1 replies, 6 likes


Jul 19 2019 Simon

If I hooked anyone with my @curry_on_conf talk (https://youtu.be/DCs0_T9BRp0) here is a great further read: https://arxiv.org/pdf/1907.07587.pdf It explains all the interesting bits I couldn't dive into! (How ML, Scientific Computing and AD all come together in #JuliaLang )
0 replies, 3 likes


Jul 19 2019 Hacker News 250

∂P: A Differentiable Programming System to Bridge ML and Scientific Computing https://arxiv.org/abs/1907.07587 (http://news.ycombinator.com/item?id=20477873)
0 replies, 3 likes


Jul 30 2019 Amir Saffari

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing by Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckas, Elliot Saba, Viral B Shah, Will Tebbutt https://arxiv.org/abs/1907.07587
0 replies, 2 likes


Jul 19 2019 Irenes (many)

Holy shit. The machines are coming for our job. (Okay, not *quite*. But on first read, it seems like a huge step. We didn't previously know that the field of differentiable programming existed.) https://arxiv.org/pdf/1907.07587.pdf
1 replies, 1 likes


Jul 23 2019 Medford Group

Cool paper on automatic differentiation for scientific computing in @JuliaLanguage! https://arxiv.org/abs/1907.07587 @johnkitchin
0 replies, 1 likes


Jul 19 2019 Brendan Meade

I want to believe (in @JuliaLanguage): https://arxiv.org/abs/1907.07587
0 replies, 1 likes


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