torchsde
dex-lang
torchsde | dex-lang | |
---|---|---|
5 | 25 | |
1,473 | 1,535 | |
2.0% | 0.3% | |
4.8 | 8.8 | |
7 months ago | 7 days ago | |
Python | Haskell | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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torchsde
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Google Research • Differentiable SDE solvers with GPU support and efficient sensitivity analysis in PyTorch. For stochastic differential equations in your deep learning models
Github: https://github.com/google-research/torchsde
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[D] Ideal deep learning library
So not just that paper, but also our follow-up papers on the same topic: Neural SDEs as Infinite-Dimensional GANs Efficient and Accurate Gradients for Neural SDEs are in fact implemented in PyTorch, specifically the torchsde library. (Disclaimer: of which I am a developer.)
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[D] Is there any way for GAN to generate arbitrary length of time series signal?
Code: SDE-GAN example in torchsde.
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[P] Final Year Computer Science Project Suggestions
If you're interested in finance then I'd recommend Neural SDEs: https://arxiv.org/abs/2102.03657 https://arxiv.org/abs/2105.13493 https://github.com/google-research/torchsde/blob/master/examples/sde_gan.py
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Simple & Fast GAN Training [D]
This may or may not fit what you're after.
dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
See: https://github.com/google-research/dex-lang/pull/969
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
What are some alternatives?
torchdyn - A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
pysindy - A package for the sparse identification of nonlinear dynamical systems from data
futhark - :boom::computer::boom: A data-parallel functional programming language
tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
julia - The Julia Programming Language
SSD-pytorch - SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
hasktorch - Tensors and neural networks in Haskell
functorch - functorch is JAX-like composable function transforms for PyTorch.
CIPs