1 citations · 1 across the 2 of their papers we have counts for
3 papers
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations
Marco Bertolini, Djork-Arné Clevert, Floriane Montanari
Most efforts in interpretability in deep learning have focused on (1) extracting explanations of a specific downstream task in relation to the input features and (2) imposing const…
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularizati…
Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network. As a mitigation str…