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**TL;DR:** Implementing block-sparse operations for faster matrix-multiplication.
Is this something worth adding to PyTorch?
Goals:
1. Faster matrix-multiplication by taking advantage of block-…
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Would this also work for LSTM? Do you have any benchmark of the method on that?
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I am not using any batch normalization during training. But, I get the following error. The feature map has been implemented in the scope of the code. It works on GPU version, but not on CPU version.
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Hi Tim!
Many thanks for this awesome repo and your [paper](http://arxiv.org/abs/1907.04840).
It is always cool when someone tries DL actually useful, accessible and more efficient!
We are build…
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1. [Binary Relevance Efficacy for Multilabel Classification](https://link.springer.com/article/10.1007/s13748-012-0030-x) > https://github.com/Gin04gh/datascience/issues/6#issuecomment-419388287
1. […
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I have trained a Recurrent network using an LSTMCell and MLP layers. But when I load the model and the weights for running the benchmark, I get "RuntimeError: output with shape [256] doesn't match the…
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I want to create a network with an LSTM layer, where the number of outputs from the LSTM layer is different from the number of its inputs. Is this possible?
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Anyone has interest to utilize the sparsity to accelerate DNNs?
I am working on the fork https://github.com/wenwei202/caffe/tree/scnn and currently, on average, achieve ~5x CPU and ~3x GPU layer-wi…
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As part of our ongoing efforts to improve the performance and accuracy of our predictive models, we need to evaluate a variety of machine learning and deep learning algorithms. Here's a list of models…
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Tested the new auto-batching on a seq2seq model and got: `RuntimeError: Magnitude of gradient is bad: inf`