Bhavya Bhatt

Current implementation uses sequential training of all the models of sigma network but it would massively speed up the computation if those models can be trained parallely.

Currently PyGlow uses tqdm progress bar api but it would be more natural to implement one just like keras (Note: to see keras implementation of progress bar see generic_utils in...

As observed during training, at the start of every epoch there appears a 0 % bar. Fix this bug which is most probably related to how the tqdm updates its...

Current implementation of fit use either x, y dataset or takes dataloader as arguments. It would be more less expensive to implement a fit method that directly load data in...

Current implementation of HSIC training process has single core computations but we can very well exploit the layer independence to train each layer on a different processor which can heavily...

After #11 is closed then we can work on documentation and website of the library for users to get their hands on the code.

Currently many of the modules is not handling exceptions and can make program crash in extreme cases.

Currently estimation methods are not appropriately implemented to get complete parallel processing of matrix operations and thus more efficient implementations should be employed to speed up the computation.

As the calculation of IP coordinates for each layer for each batch for each epoch is independent and can be massively calculated in parallel as compared to current implementation which...