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08/10/2023
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Retrosynthesis consists of breaking down a chemical compound recursively...
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02/03/2023
From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology
In pre-clinical pathology, there is a paradox between the abundance of r...
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02/20/2022
Equivariant Graph Attention Networks for Molecular Property Prediction
Learning and reasoning about 3D molecular structures with varying size i...
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02/15/2022
Unsupervised Learning of Group Invariant and Equivariant Representations
Equivariant neural networks, whose hidden features transform according t...
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05/11/2021
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Rationalizing which parts of a molecule drive the predictions of a molec...
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04/20/2021
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Recently, there has been great success in applying deep neural networks ...
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03/30/2021
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) ...
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01/05/2021
Auto-Encoding Molecular Conformations
In this work we introduce an Autoencoder for molecular conformations. Ou...
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10/09/2020
Gini in a Bottleneck: Gotta Train Me the Right Way
Due to the nature of deep learning approaches, it is inherently difficul...
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11/23/2017
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful framework for gene...
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11/23/2015
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
We introduce the "exponential linear unit" (ELU) which speeds up learnin...
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02/23/2015
