114 citations · 342 across the 6 of their papers we have counts for
6 papers
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang +12
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., wh…
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic +1
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph alg…
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux +1
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework…
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Meng Qu, Junkun Chen, Louis-Pascal Xhonneux +2
This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generali…
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux +1
This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation…
Continuous Graph Neural Networks
Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing g…