Interpretable Classification via Supervised Variational Autoencoders and Differentiable Decision Trees

By Eleanor Quint, Garrett Wirka, Jacob Williams, S. Scott, N. V. Vinodchandran. 2018

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EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction (2019, background): “The second direction proposes new models and architectures that exhibit predictive performance close to deep learning ones, while being interpretable by the users (see for example [7, 27, 2, 19]).”
Improved metagenome binning and assembly using deep variational autoencoders (2021, background): “VAEs have been explored in many aspects such as unsupervised learning 25 , semi-supervised learning 26 and supervised learning 27 as well as many different architectures such as the ladder VAE 28 , beta-VAE 29 , Info-VAE 30 and others.”