34 citations · 61 across the 5 of their papers we have counts for
5 papers
Hyper-Dimensional Fingerprints as Molecular Representations
Jonas Teufel, Luca Torresi, André Eberhard +1
Computational molecular representations underpin virtual screening, property prediction, and materials discovery. Conventional fingerprints are efficient and deterministic but lose…
Building Deep Graph Predictors with Graph Imitation Learning
André Eberhard, Gerhard Neumann, Pascal Friederich
Recent years have seen substantial progress in neural generation of text, images, and audio, supported by mature training pipelines and large-scale optimization. For graphs, howeve…
Actively Learning Costly Reward Functions for Reinforcement Learning
André Eberhard, Houssam Metni, Georg Fahland +2
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent…
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design n…
Implementing graph neural networks with TensorFlow-Keras
Patrick Reiser, Andre Eberhard, Pascal Friederich
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution…