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Hi! I'm really enjoying and exploiting your team's implementation for my research.
Recently, I started to study variational inference to delve into entropy regularized policy algorithms,
by taki…
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Which papers are behind the variational inference and the sparse approach of gpytorch?
on the doc site are following references given:
> Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilia…
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Hi, has anybody succeeded in replicating the results of the paper Doubly Stochastic Variational Inference for Deep Gaussian Processes by Salimbeni and Deisenroth in GPyTorch? There is an example DeepG…
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I have been thinking a bit about hierarchically structured data and how to combine the ideas in scVI with nested random effects models.
A very typical nested experimental design in the field is wit…
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We are currently not able to compute the log probability `p(x_i | theta)` for each observation in Turing. Instead, we always compute `sum_i log p(x_i, theta)` which makes a lot of sense from an infere…
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arXiv论文跟踪
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#775
Key idea:
Relaxes the mean-field approximation (commonly used in Stochastic Variational Inference) to allow arbitrary dependencies between global parameters and local hidden variables, produ…
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Please make sure that this is a bug. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and…
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Using [`monte_carlo_csiszar_f_divergence`](https://www.tensorflow.org/probability/api_docs/python/tfp/vi/monte_carlo_csiszar_f_divergence) to perform inference for a single distribution works as expec…
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Hi,
Thanks for sharing this library. I was wondering whether we can fit the states on a collection of time series instead of just one as you do on the examples.
Thanks.