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1.**Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.**
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1.**Generating Interpretable Images with Controllable Structure**
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1.**Supervised topic models for clinical interpretability.**
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1.**An Effective and Interpretable Method for Document Classification**
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1.**Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks**
*Anna Potapenko, Artem Popov, and Konstantin Vorontsov*.
1.**Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks.**
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*Fong, Ruth C and Vedaldi, Andrea*.
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