Graph Neural Networks User Group (hosted by AWS and NVIDIA)
We look forward to building community and sharing our interest in GNNs. While this is hosted by AWS and NVIDIA, all are welcome!
Learning about graphs has emerged as one of the hottest area of research in the field of machine learning and artificial intelligence. In a span of few years, graph neural network (GNN) has quickly expanded from more theoretically-bent and small-scale studies to many diverse applications, including classic graph applications (e.g. information retrieval, recommendations, fraud detections, knowledge graph, healthcare), scientific problems (chemistry, bioinformatics, drug discoveries, material science, physics, etc.), and fundamental problems in computer science and engineering (computer vision, natural language processing, computer graphics, circuitry design, reinforcement learning, etc.), and the horizon keeps on expanding.
This online seminar series focuses on learning about graphs. Its goal is to bring together GNN ML researchers, system researchers/builders of GNN platforms and applied data scientists using graph datasets across different fields and organizations in order to disseminate fresh results, stimulate new ideas, and exchange best practices.
We will hold these meetings monthly.
4:00 – 4:15 PM (PDT): DGL 0.7 release(Dr. Minjie Wang, Amazon)
4:15 – 4:30 PM (PDT): Storing Node Features in GPU memory to speedup billion-scale GNN training (Dr. Dominique LaSalle, NVIDIA)
4:30 – 5:00 PM (PDT): Locally Private Graph Neural Networks (Sina Sajadmanesh, Idiap Research Institute, Switzerland).
5:00 – 5:30 PM (PDT): Graph Embedding and Application in Meituan (Mengdi Zhang, Meituan).