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License: MIT License
Blind source separation based on the probabilistic tensor factorisation framework
License: MIT License
I have one data set where the source extraction looks good and another where it doesn't do so well, and I'm curious to explore how this relates to the prior distributions I picked and the hyperparameters the model found for those priors. Thank you!
Thank you for developing this package and publishing it in this easily-installable and open way!
I was trying out the method on our lightsheet data, and the results on small patches look promising. However, if I try to apply it to a bigger chunk, TensorFlow complains:
ValueError: Cannot create a tensor proto whose content is larger than 2GB.
I already have algorithms in place to stitch spatial filers extracted from overlapping patches from another method (CNMF), however due to the large signal contamination from out-of-focus planes in the lightsheet, taking into account bigger areas with more planes would yield better results, if the computational costs do not become prohibitively large.
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