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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/04/23 01:58:41 UTC

[GitHub] [incubator-mxnet] xidulu commented on issue #18140: Reparameterization trick for Gamma distribution

xidulu commented on issue #18140:
URL: https://github.com/apache/incubator-mxnet/issues/18140#issuecomment-618131555


   Currently, pathwise gradient is only implemented for `mx.np.random.{normal, gumbel, logistic, weibull, exponential, pareto}` in the backend.
   
   We are planning to implement (in C++ backend) implicit reparam grad for Gamma related distribution in the future, which is extremely useful, as you pointed out, in scenarios like `BBVI for LDA`.
   
   Another possible solution, is to wrap the sampling Op as a CustomOp, which allows you to manually define the backward computation with Python.
   https://mxnet.apache.org/api/python/docs/tutorials/extend/customop.html


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