SDE
score_sde
SDE | score_sde | |
---|---|---|
1 | 6 | |
153 | 1,242 | |
0.0% | - | |
0.0 | 0.0 | |
almost 3 years ago | over 1 year ago | |
MATLAB | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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SDE
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Ask HN: Good Book on Stochastic Differential Equations?
I am not an expert in SDEs (my background is machine learning) but if I wanted to dig more into the subject, this is where I personally would start. Code [1] and pdf [2] for the book are available.
Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press. Cambridge, UK.
[1] https://github.com/AaltoML/SDE
[2] https://users.aalto.fi/~asolin/sde-book/sde-book.pdf
score_sde
- Ask HN: How to get back into AI?
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[D] Variance of sampling in diffusion models
Perhaps the ODE interpretation would be helpful (see here and here) which turns DDPMs into neural ODEs using the Fokker-Planck equation so after the initial starting noise, the sampling process is deterministic. If samples are noisy even with the full number of steps then you might need to increase the number of steps further.
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[D] Why is the diffution model so powerful? but the math behind it is so simple.
Turns out that diffusion models also define a certain differential equation, making it a neural ODE. Then you can just integrate the ODE in the other direction to get the exact inverse for the DDPM (it's not entirely exact b/c of numerical error in the solver, but close enough)
- [D] Are DDPMs a variation on Score Based Generative Modeling? Or is there a fundemental difference between the two?
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Diffusion Models Beat GANs on Image Synthesis
This new approach to generative modelling looks very intriguing.
In a similar ilk, there's this ICLR paper from this year using stochastic differential equations for generative modelling: https://arxiv.org/abs/2011.13456
- [D] Efficient, concurrent input pipelines in JAX?
What are some alternatives?
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
guided-diffusion
gramm - Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
pytorch-generative - Easy generative modeling in PyTorch.
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
score_sde_pytorch - PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy