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I could not find anything in the docs about how to handle different frequencies of time series. I have a Dataset A with monthly data that i want to use to predict the values from Dataset B that contains quarterly based data. So the target value e.g. quarter 1 is based on the values from month 1-3.

Dataset A (Features):

| Month | Value1 | Value2 | Value3 |
| ------------- | ------------- |

Context

We would like to add torch::nn::functional::gumbel_softmax to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.gumbel_softmax.

Steps

• Add torch::nn::GumbelSoftmaxOptions to torch/csrc/api/include/torch/nn/options/activation.h (add this file if it doesn’t exist), which should include the following parameters (based on

Line 1137 of the Caffe.Proto states "By default, SliceLayer concatenates blobs along the "channels" axis (1)."

Yet, the documentation on http://caffe.berkeleyvision.org/tutorial/layers/slice.html states, "The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices." which seems to be

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.

Feedback from some workshop is that we should pay more attention to the quality and working status of the examples we have in the repossitory to help people.

• Have CI running on examples #2353
• Ensure examples works with latest stable version #2351
• Improve documentation by referring to examples
• Once v0.6, stick examples to it