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Pytorch Geometric with Matthias Fey

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Manage episode 313477744 series 3272662
Content provided by minhaaj rehman and Minhaaj rehman. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by minhaaj rehman and Minhaaj rehman or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

  continue reading

37 episodes

Artwork
iconShare
 
Manage episode 313477744 series 3272662
Content provided by minhaaj rehman and Minhaaj rehman. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by minhaaj rehman and Minhaaj rehman or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

  continue reading

37 episodes

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