A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
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Updated
Oct 6, 2020
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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 Theano
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
In X-ray crystallography, the most important prior distributions include two special cases of the generalized gamma distribtion. I am very keen to try this parameterization of the variational distritribution in my research project. How hard would it be for the TFP devs to implement this distr
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
Probabilistic data structures for processing continuous, unbounded streams.
Awesome-pytorch-list 翻译工作进行中......
Bayesian inference with probabilistic programming.
BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
PyStan, the Python interface to Stan
Algorithm is a library of tools that is used to create intelligent applications.
Per a question asked by a user in the forum, it would be nice to have a tutorial/example for this type of regression. I searched for some examples available and found some nice ones below.
Resources to learn more about Machine Learning and Artificial Intelligence
Probabilistic programming for the web
Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code
The current example on MDN from Edward tutorials needs small modifications to run on edward2. Documentation covering these modifications will be appreciated.
A probabilistic programming language
Experimental tensor-typed deep learning
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
Tests not only allow to make sure the code is working as expected but they can also be used to make sure that the documentation and examples are up to date.
Documentation should be quite fast to test so can run with the CI after every push. We might need to do
Probabilistic programming via source rewriting
probmods 2: electric boogaloo
Sample code for the Model-Based Machine Learning book.
The Design and Implementation of Probabilistic Programming Languages
Functional tensors for probabilistic programming
In the scorer, argument x should be checked to lie in the interval [a,b].
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
BOPP: Bayesian Optimization for Probabilistic Programs
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Currently our non-notebook examples are manually included in the examples webpage via custom
.rstfiles intutorial/source/. As the number of examples increases, it would be better to follow NumPyro's approach and generate HTML pages automatically withsphinx_gallery. This would make examples easier t