This repository contains code for running both the manifold GPLVM (Jensen et al. 2020) and Bayesian GPFA (Jensen and Kao et al. 2021) and is currently still in active development. Currently, the master branch can be used for mGPLVM and the bGPFA branch for bGPFA.
# inside virtual environment pip install -e .To run on GPU, it may be necessary to first install pytorch with GPU support.
- pytorch
- numpy
- scipy
- matplotlib
This library uses yapf for autoformatting. To autoformat all files in this directory:
yapf -ir .py.test@inproceedings{jensen2020manifold, author = {Jensen, Kristopher and Kao, Ta-Chu and Tripodi, Marco and Hennequin, Guillaume}, booktitle = {Advances in Neural Information Processing Systems}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin}, pages = {22580--22592}, title = {Manifold GPLVMs for discovering non-Euclidean latent structure in neural data}, volume = {33}, year = {2020} } @inproceedings{ jensen2021scalable, title={Scalable Bayesian {GPFA} with automatic relevance determination and discrete noise models}, author={Kristopher T Jensen and Ta-Chu Kao and Jasmine Talia Stone and Guillaume Hennequin}, booktitle={Advances in Neural Information Processing Systems}, editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan}, year={2021}, }