Assistant Professor at Mila. Working on geometric deep learning, AI for small molecule/protein design, knowledge graphs, generative models
Jan 21, 2023 • 8 tweets • 4 min read
Five papers have been accepted to #ICLR2013 in my group (including one oral presentation), covering topics from combining pretrained LMs with GNNs, deep generative models and pretraining methods for drug discovery.
1) An oral presentation. We proposed an effective and efficient method for combining pretrained LLMs and GNNs on large-scale text-attributed graphs via variational EM. The first place on 3 tasks of node property prediction on OGB leaderboards.
Five papers related to GNNs are accepted to #NeurIPS2021 in my group, ranging from knowledge graph reasoning, drug discovery, scene graph generation and algorithmic reasoning. Congrats to all my students and collaborators.
1) Zhu et al. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. arxiv.org/abs/2106.06935
A state-of-the-art algorithm for link prediction based on GNNs in both transductive and inductive settings.
