References

Code

sci-vae is designed for Python >=3.6 and requires the following libraries, which will be automatically installed:

Library

Version

Reference

keras

>= 2.4.0

@misc{chollet2015keras, title={Keras}, author={Chollet, Franc{c}ois and others}, year={2015}, howpublished={url{https://keras.io}},}

Tensorflow and tensorflow-probability

>= 2.1.0

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 265–283. https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf

NumPy

>= 1.9.0

Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 0.1038/s41586-020-2649-2

pandas

>= 1.0.3

 https://zenodo.org/badge/DOI/10.5281/zenodo.3715232.svg

scikit-learn

>= 0.22.2

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830.

pyswarm

>= 0.6

https://github.com/tisimst/pyswarm

matplotlib

>= 3.3.3

 https://zenodo.org/badge/DOI/10.5281/zenodo.3264781.svg

seaborn

>= 0.11.1

 https://zenodo.org/badge/DOI/10.5281/zenodo.4379347.svg

We strongly encourage you to cite the other python packages. Note pyswarm and scikit-learn are only used when using the optimiser component (not your standard VAE) so these can be omitted if you don’t use them. Similarly, matplotlib and seaborn may not necessarily be used, so it depends on what aspects of this project is used in your work.

Literature

Label

Reference

VAE paper

Kingma, Diederik P, and Max Welling. 2014. “Auto-Encoding Variational Bayes.” In International Conference on Learning Representations.

Info VAE

Zhao, S., Song, J., & Ermon, S. (2018). InfoVAE: Information Maximizing Variational Autoencoders. ArXiv:1706.02262 [Cs, Stat]. http://arxiv.org/abs/1706.02262

VAEs applied to cancer

Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & Liò, P. (2019). Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.01205

Data

Name

Link

Reference

Encode

https://www.encodeproject.org/help/citing-encode/

ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414), 57–74. https://doi.org/10.1038/nature11247

MNIST

http://yann.lecun.com/exdb/mnist/

  1. LeCun, L. Bottou, Y. Bengio, and P. Haffner. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE, 86(11):2278-2324, November 1998. [on-line version]

Iris dataset

https://archive.ics.uci.edu/ml/datasets/Iris/

Fisher,R.A. “The use of multiple measurements in taxonomic problems” Annual Eugenics, 7, Part II, 179-188 (1936); Edgar Anderson (1936). “The species problem in Iris”. Annals of the Missouri Botanical Garden. 23 (3): 457–509. doi:10.2307/2394164

Environments

This will be user dependent but don’t forget to cite anaconda if you use it or reticulate if you use R.