A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
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Updated
Dec 30, 2021
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A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
Are there any plans to add a Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) to TFP? Those are usually very common distributions in other packages, and it shouldn't be hard to implement.
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Ankit Shah and I are trying to use Gen to support a project and would love the addition of a dirichlet distribution
Awesome-pytorch-list 翻译工作进行中......
Bayesian inference with probabilistic programming.
Probabilistic data structures for processing continuous, unbounded streams.
Dear Numpyro developers,
Please develop Euler Maruyama features in numpyro similar to features found in PyMC.
Thanks alot.
PyStan, the Python interface to Stan
BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Resources to learn more about Machine Learning and Artificial Intelligence
Algorithm is a library of tools that is used to create intelligent applications.
High-quality implementations of standard and SOTA methods on a variety of tasks.
Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code
Probabilistic programming for the web
The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.
Probabilistic programming via source rewriting
Hi,
Looks like there is support for lots of common distribution. There are a handful of other distributions which are not presently supported but could (fingers crossed) be easily implemented. Looking at [Stan's Function Reference] I see...
A probabilistic programming language
There are a variety of interesting optimisations that can be performed on kernels of the form
k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) + ... + w_L k_L(x, z)A naive recursive implementation in terms of the current Sum and Scaled kernels hides opportunities for parallelism in the computation of each term, and the summation over terms.
Notable examples of kernels with th
Rather than trying to rebuild all functionality from Distributions.jl, we're first focusing on reimplementing logdensity (logpdf in Distributions), and delegating most other functions to the current Distributions implementations.
So for example, we have
distproxy(d::Normal{(:μ, :σ)}) = Dists.Normal(d.μ, d.σ)This makes some functions in Distributions.jl available through
Experimental tensor-typed deep learning
Hey
You are here because you considered contributing to blackjax for at least a split second. Thank you! But sometimes we are just not quite sure what to work on/don't want to bother the maintainer. We've been there. That is why we put together a list of the projects that are up for grabs on blackjax.
**You can pick any of these, open an issue to signal you are working on it and
probmods 2: electric boogaloo
Sample code for the Model-Based Machine Learning book.
Add a description, image, and links to the probabilistic-programming topic page so that developers can more easily learn about it.
To associate your repository with the probabilistic-programming topic, visit your repo's landing page and select "manage topics."
NumPyro now has several excellent introductory examples with no direct counterparts in Pyro. Porting one of these to Pyro would be a great way for someone to simultaneously learn more about Bayesian data analysis and make a valuable open source contribution.
If you are reading this and want to give one of them a try, please leave a comment here so that other peo