Category: Deep Learning

Fast (Differentiable) Soft DTW for PyTorch using CUDA

Dynamic time warping (DTW) is a dynamic programming algorithm which aims to find the dissimilarity between two time-series. This algorithm was originally applied towards speech recognition. In ICML 2017, Marco Cuturi and Mathieu Blondel proposed a differentiable formulation of this algorithm that’s very helpful in optimization problems involving temporal sequences. They call this differentiable formulation …

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Masked Tensor Operations in PyTorch

As far as I know, PyTorch does not inherently have masked tensor operations (such as those available in The other day, I needed to do some aggregation operations on a tensor while ignoring the masked elements in the operations. Specifically, I needed to do a mean() along a specific dimension, but ignore the masked …

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Use PyTorch’s DataLoader with Variable Length Sequences for LSTM/GRU

When I first started using PyTorch to implement recurrent neural networks (RNN), I faced a small issue when I was trying to use DataLoader in conjunction with variable-length sequences. What I specifically wanted to do was to automate the process of distributing training data among multiple graphics cards. Even though there are numerous examples online …

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