Kamer Ali Yuksel

Thanks for your response. The idea is actually intended to better use the limited computational budget as it tries to prevent visiting the same local minimas again and would rather...

Thank you very much for your response. I apologize for my misunderstanding. I thought that you were trying to make it more sparse and eliminate very low, negligible weights. I...

@robertmartin8 I have tried to learn how to sparsify via the following https://github.com/deep-spin/entmax I actually want something where I can learn both sparsity and the temperature of a sparsemax function...

FLAML compatibility could be good if that doesn’t exist.

Would this be a right implementation? ``` class DropBlock1D(nn.Module): def __init__(self, drop_prob = 0.2, block_size = 3): super(DropBlock1D, self).__init__() self.p = drop_prob self.bs = block_size def forward(self, x): if not...

@miguelvr Thanks for your response. I now checked many of the existing unit tests. Also, using it already in two projects where in both it contributed. I am not very...

- https://github.com/Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks - http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks.pdf

For the weekend imputation problem of the dataset I have shared, one can create a training set that consists of data from Monday to Friday at each week. Then, impute...

Dear @avik-pal & @Aniket1998, I have attempted making an implementation of what I have described as a potential solution to above mentioned weekend imputation problem. I would be more than...

@hellojinwoo You should use a sliding windows so have overlapped parts for data augmentation (increasing the amount of the data that you have). However, the code here would only cluster...