rtqichen/torchdiffeq
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Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
7 mentions on Hacker News by 6 people: 2 stories, 5 comments · first 2018-11-14 · latest 2020-01-24
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…github.com/scipy/scipy/tree/master/scipy/integrate/t... . Same with other libraries like torchdiffeq: https://github.com/rtqichen/torchdiffeq/tree/master/tests . So Julia's testing culture is now strict enough that things that are commonly…
comment by ChrisRackauckas · 2020-01-24 · in Haskell Problems for a New Decade -
There is a PyTorch implementation by the authors https://github.com/rtqichen/torchdiffeq and there is a Julia implementation due: https://news.ycombinator.com/item?id=18676986#18691054 . As for TensorFlow, we will have to wait and see if…
comment by jsinai · 2019-01-23 · in Neural ODE's: Understanding how they model data -
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PyTorch Implementation of Differentiable ODE Solversstory by quantumwoke · 2019-01-23 · 0 comments
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…adjoint sensitivities (the most scalable variant of autodiff for training scalar losses). [ https://github.com/rtqichen/torchdiffeq ] In the paper we talk a bit about how you can now more easily train Possion process likelihoods, which…
comment by duvenaud · 2018-12-14 · in Neural Ordinary Differential Equations -
Senior author here, I'm happy to answer any questions. We just released source code: https://github.com/rtqichen/torchdiffeq . This includes PyTorch implementations of adaptive ODE solvers that can be differentiated through automatically.…
comment by duvenaud · 2018-12-14 · in Neural Ordinary Differential Equations -
…HN about a month ago of a PyTorch implementation for ODEs (the name for this new NN design) here: https://github.com/rtqichen/torchdiffeq/blob/master/README.m...
comment by nmstoker · 2018-12-12 · in A new neural network design could overcome challenges in AI -
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PyTorch Implementation of Differentiable ODE Solversstory by stablemap · 2018-11-14 · 0 comments