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Geometric Deep Learning, Graph Neural Networks for Drug Design. Equivariant Neural Networks for Molecular Simulation. Knowledge Graph Construction and Reasoning, Natural Language Understanding.
- Graph Representation Learning
In this course, I will introduce the latest progress on...
- Publications
Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng and Jian-Yun...
- Talks and Presentations
Invited talk on “Graph Representation Learning for Drug...
- Graph Representation Learning
Jian Tang (唐建）. Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI Chair. Verified email at hec.ca - Homepage. Geometric deep learning AI for molecule/protein design...
The largest academic lab on deep learning and reinforcement learning. >30 professors (14 core member), ~ 300 students. Multiple Postdoc, Ph.D., Master, and Interns positions are available. Why graphs? Graphs are a general language for describing and modeling complex systems. Tutorial on Graph Representation Learning, AAAI 2019. Graph!
Tang’s main research interests are deep generative models and graph machine learning, and their applications to drug discovery. He is an international leader in graph machine learning, and LINE, his node representation method, has been widely recognized and cited more than five thousand times.
