Daniel Steinberg

https://arxiv.org/abs/1803.04386 Eqn. (4) could be implemented as an alternative to `_sample_W` in `layers.py`, caveats: - This maybe be hard getting to work with full-covariance weights - This may require the...

At the moment we assume the Dense layers can only work with float32 inputs/distributions. Do we want to support other types?

Make a VAE?

2

This *may* not be the best framework for it, but it would be a good exercise to see how it looks compared to competing frameworks, e.g. Keras, Edward.

right now we are just testing shapes in many cases, like in `test_dense_embeddings`.

this is because sometimes `None` or `?` dimensions are propagated in unexpected ways, especially when we have reshapes (with -1)!

For high dimensional data, and input data with different types - Low dimensional embedding - Categorical embedding - Joint embedding

Compare different methods on a high dimensional problem: - Random - Learned mean - Learned Normal - Learned Low rank Gaussian

``` To solve the libclusterpy.dylib vs libclusterpy.so issue I found this code snippet which I included in the CMakeLists.txt just after 'library build instructions'. if(APPLE) set(CMAKE_SHARED_LIBRARY_SUFFIX ".so") endif(APPLE) This forced...