4 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Scientific intuition inspired by machine learning generated hypotheses
Pascal Friederich, Mario Krenn, Isaac Tamblyn +1
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in…
Neural Message Passing on High Order Paths
Daniel Flam-Shepherd, Tony Wu, Pascal Friederich +1
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as func…
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam +2
The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncove…