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pyro

pyro-ppl / pyro

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Port NumPyro introductory examples to Pyro

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eb8680
eb8680 commented Dec 14, 2021

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

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help wanted Examples good first issue
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GPflow / GPflow

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Can we use type hints for our multiple dispatch?

jesnie
jesnie commented Feb 4, 2022

Feature request

In several places we use multiple dispatch. Right now the types to dispatch on are configured separately. Could we infer the types to dispatch on from Python type hints? That would simplify the code.

Motivation

Instead of:

@dispatch.expectation.register(Gaussian, kernels.Sum, InducingPoints, NoneType, NoneType)
def expectation_gaussian_sum_inducingpoints(
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enhancement good first issue
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JavierAntoran / Bayesian-Neural-Networks

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[Question] BBB vs BBB w/ Local Reparameterization

4
danielkelshaw
danielkelshaw commented Apr 30, 2020

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

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good first issue question

kumar-shridhar / PyTorch-BayesianCNN

Star 1.1k

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

python pytorch bayesian-network image-recognition convolutional-neural-networks bayesian-inference bayes bayesian-networks variational-inference bayesian-statistics bayesian-neural-networks variational-bayes bayesian-deep-learning pytorch-cnn bayesian-convnets bayes-by-backprop aleatoric-uncertainties
  • Updated Feb 5, 2021
  • Python

FranxYao / Deep-Generative-Models-for-Natural-Language-Processing

Star 304

DGMs for NLP. A roadmap.

natural-language-processing parsing decoding text-generation generative-text generative-model graphical-models mcmc language-model generative-adversarial-networks variational-inference markov-chain-monte-carlo latent-variable-models normalizing-flows structured-prediction discrete-structures generative-models variational-autoencoders approximate-inference gradient-estimation
  • Updated Jun 14, 2022

omerbsezer / Generative_Models_Tutorial_with_Demo

Star 301

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

tutorial generative-adversarial-network dcgan generative-model gaussian-mixture-models auto-regressive-model gans bayesian-classifiers variational-inference tutorial-code variational-autoencoder cyclegan
  • Updated Jan 21, 2019
  • Jupyter Notebook

hoangcuong2011 / Good-Papers

Star 265

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

machine-learning natural-language-processing computer-vision deep-learning neural-network semi-supervised-learning convolutional-networks neural-machine-translation representation-learning gaussian-processes variational-inference kernel-learning variational-autoencoders deep-kernel-learning
  • Updated Jun 5, 2019

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