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

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