Wasserstein loss layer/criterion
Is it possible to keep intermediate results of forward for backward?
How to use pack_padded_sequence in seq2seq models
GPU slower than CPU on a simple RNN test code
Building a custom loss function in pytorch
Different Learning Rates within a Model
Tracking down a suspected memory leak
Backprop Through sparse_dense_matmul
Implementation of Batch Renormalization fails unexpectedly (Segementation fault core dumped)
Cannot Unsqueeze Empty Tensor
When backward() can be safely omitted?
(Newbie Question) Getting the gradient of output with respect to the input
How to exclude Embedding layer from Model.parameters()?
Autograd Function vs nn.Module?
Operation between Tensor and Variable
How to install PyTorch such that can use GPUs
No broadcasting for Variable?
Is it possible to make a function transparent between cpu and gpu?
How to manipulate layer parameters by it's names?
Computing the gradients for batch renormalization
Cannot install PyTorch on Ubuntu14.04 via pip
Why can't Adagrad work within my implementation?
Custom Function - Open questions
Convert pixel wise class tensor to image segmentation
How can I do element-wise batch matrix multiplication?
Can we use pre-trained word embeddings for weight initialization in nn.Embedding?
How to split the Variables along Batch Dimension to several Variables?
Run two different jobs in parallel on same GPUs, I got my GPU locked up
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