6 citations · 9 across the 5 of their papers we have counts for
4 papers
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…
Auto-Encoding Molecular Conformations
Robin Winter, Frank Noé, Djork-Arné Clevert
In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformatio…
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…
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Robin Winter, Djork-Arné Clevert
Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation proces…