https://onlinelibrary.wiley.com/doi/10.1111/php.13866
Görlitz, M., Justen, L., Rochette, P. J., Buonanno, M., Welch, D., Kleiman, N. J., Eadie, E., Kaidzu, S., Bradshaw, W. J., Javorsky, E., Cridland, N., Galor, A., Guttmann, M., Meinke, M. C., Schleusener, J., Jensen, P., Söderberg, P., Yamano, N., Nishigori, C., . . . Esvelt, K. (2023). Assessing the safety of new germicidal far‐UVC technologies. Photochemistry and Photobiology, 100(3), 501–520.
https://doi.org/10.1111/php.13866
Has detailed setup for ELBO
https://arxiv.org/abs/1906.02691
Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392. https://doi.org/10.1561/2200000056
https://www.biorxiv.org/content/10.1101/2024.05.08.593025v1
Chen, J., Xiong, C., Sun, Q., Wang, G. W., Gupta, G. P., Halder, A., Li, Y., & Li, D. (2024). Investigating spatial dynamics in spatial omics data with StarTrail. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.05.08.593025
https://arxiv.org/abs/1803.00567
explains the dual problem well
Peyré, G., & Cuturi, M. (2018, March 1). Computational Optimal transport. arXiv.org. https://arxiv.org/abs/1803.00567
tutorial_on_optimal_transport.pdf
https://arxiv.org/abs/2312.07511
Duval, A., Mathis, S., V., Joshi, C. K., Schmidt, V., Miret, S., Malliaros, F. D., Cohen, T., Liò, P., Bengio, Y., & Bronstein, M. (2023, December 12). A Hitchhiker’s guide to Geometric GNNs for 3D atomic Systems. arXiv.org. https://arxiv.org/abs/2312.07511
Good intuition on spherical harmonics
https://arxiv.org/abs/1906.02691
Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392. https://doi.org/10.1561/2200000056
https://www.nature.com/articles/s43586-022-00184-w
Greenacre, M., Groenen, P. J. F., Hastie, T., D’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1).
https://doi.org/10.1038/s43586-022-00184-w
https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444
McGillivray, P., Clarke, D., Meyerson, W., Zhang, J., Lee, D., Gu, M., Kumar, S., Zhou, H., & Gerstein, M. (2018). Network analysis as a grand unifier in biomedical data science. Annual Review of Biomedical Data Science, 1(1), 153–180.
https://doi.org/10.1146/annurev-biodatasci-080917-013444
linkstream2 microblog 