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Most of methods in the list will be implemented in the order.
- inference for Sparse Gaussian process regression (based on JMLR 2005 "A unifying view of sparse approximate Gaussian process regression…
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# GP
- [GP for big data: Hensmen (2013)][2]
- [Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models: Yu (2017)][3]
# SVI
- [Variational Auto-encoder][4]…
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> [!NOTE]
> If you have a request to support a specific method, or would like to see priority of one of the listed methods, please open a separate issue, so it won't get buried in this thread. Base…
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# 📚 Documentation/Examples
@vr308 I was trying to implement this paper https://arxiv.org/pdf/2202.12979v1.pdf using the example provided (Gaussian_Process_Latent_Variable_Models_with_Stochastic_Varia…
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Need a background on variational autoencoders.
Suggestions:
- Black box variational inference for state space models (http://arxiv.org/abs/1511.07367).
- Possibly a better explanation.
- Stochastic …
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The issue is a bit similar to #1769 .
I have a program where compilation takes roughly around 10-15 minutes on each run. The program is available [here](https://github.com/ahmadsalim/numpyro/blob/…
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It would be worth having something generic for all things related to stochastic approximations, to be separated from variational inference itself. E.g., a sgd class to have different stochastic gradie…
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Link: [Arxiv](https://arxiv.org/pdf/1805.00909.pdf)
This is one of the important paper that link MBRL with Variational Inference, published in 2018/05
Problem:
> the connection between reinfo…
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It would be great if someone implemented partial_fit for BayesianGaussianMixture using mean field [stochastic variational inference.](http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf) Fittin…
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## Description
I'd like to suggest the implementation of implicit reparameterization gradients, as described in the paper [1], for the Gamma distribution: ndarray.sample_gamma and symbol.sample_gamma…