Pytorch Geometric with Matthias Fey
Matthias Fey is the creator of the Pytorch Geometric library and a postdoctoral researcher in deep learning at TU Dortmund Germany. He is a core contributor to the Open Graph Benchmark dataset initiative in collaboration with Stanford University Professor Jure Leskovec. 00:00 Intro 00:50 Pytorch Geometric Inception 02:57 Graph NNs vs CNNs, Transformers, RNNs 05:00 Implementation of GNNs as an extension of other ANNs 08:15 Image Synthesis from Textual Inputs as GNNs 10:48 Image classification Implementations on augmented Data in GNNs 13:40 Multimodal Data implementation in GNNs 16:25 Computational complexity of GNN Models 18:55 GNNAuto Scale Paper, Big Data Scalability 24:39 Open Graph Benchmark Dataset Initiative with Stanford, Jure Leskovec and Large Networks 30:14 PyG in production, Biology, Chemistry and Fraud Detection 33:10 Solving Cold Start Problem in Recommender Systems using GNNs 38:21 German Football League, Bundesliga & Playing in Best team of Worst League 41:54 Pytorch Geometric in ICLR and NeurIPS and rise in GNN-based papers 43:27 Intrusion Detection, Anomaly Detection, and Social Network Monitoring as GNN implementation 46:10 Raw data conversion to Graph format as Input in PyG 50:00 Boilerplate templates for PyG for Citizen Data Scientists 53:37 GUI for beginners and Get Started Wizards 56:43 AutoML for PyG and timeline for Tensorflow Version 01:02:40 Explainability concerns in PyG and GNNs in general 01:04:40 CSV files in PyG and Structured Data Explainability 01:06:32 Playing Bass, Octoberfest & 99 Red Balloons 01:09:50 Collaboration with Stanford, OGB & Core Team 01:15:25 Leaderboards on Benchmark Datasets at OGB Website, Arvix Dataset 01:17:11 Datasets from outside Stanford, Harvard, Facebook etc 01:19:00 Kaggle vs Self-owned Competition Platform 01:20:00 Deploying Arvix Model for Recommendation of Papers 01:22:40 Future Directions of Research 01:26:00 Collaborations, Jurgen Schmidthuber & Combined Research 01:27:30 Sharing Office with a Dog, 2 Rabbits and How to train Cats