Ryutaro Tanno: Neural Networks and Decision Trees

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). We demonstrate that, whilst achieving competitive performance on classification and regression datasets, ANTs benefit from (i) lightweight inference via conditional computation, (ii) increased interpretability via hierarchical separation of features e.g. learning meaningful class associations, such as separating natural vs. man-made objects, and (iii) a mechanism to adapt the architecture to the size and complexity of the training dataset.

Bio: Ryutaro Tanno is a 3rd year PhD student at UCL on a Microsoft Research scholarship. After completing MASt in Mathematics, and MPhil from Computational and Biological Learning group in university of Cambridge, he started his Phd in 2015 under the supervision of Daniel C. Alexander at University College London and Antonio Criminisi of Microsoft Research Cambridge. His main interest lies in developing high-performance machine learning algorithms which are more interpretable and safer to use in healthcare applications. He received a best paper award in MICCAI 2017, the largest international conference on machine learning for medical imaging applications.

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