score_sde VS SDE

Compare score_sde vs SDE and see what are their differences.

score_sde

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) (by yang-song)

SDE

Example codes for the book Applied Stochastic Differential Equations (by AaltoML)
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score_sde SDE
6 1
1,242 153
- 0.0%
0.0 0.0
over 1 year ago over 2 years ago
Jupyter Notebook MATLAB
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

score_sde

Posts with mentions or reviews of score_sde. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-10.

SDE

Posts with mentions or reviews of SDE. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning SDE yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing score_sde and SDE you can also consider the following projects:

guided-diffusion

pytorch-generative - Easy generative modeling in PyTorch.

Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.

score_sde_pytorch - PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models

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.

pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy

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.

course-content - NMA Computational Neuroscience course

NNfSiX - Neural Networks from Scratch in various programming languages