Mettez-vous hors ligne avec l'application Player FM !
Pytorch Geometric with Matthias Fey
Manage episode 313477744 series 3272662
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
37 episodes
Manage episode 313477744 series 3272662
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
37 episodes
Tous les épisodes
×Bienvenue sur Lecteur FM!
Lecteur FM recherche sur Internet des podcasts de haute qualité que vous pourrez apprécier dès maintenant. C'est la meilleure application de podcast et fonctionne sur Android, iPhone et le Web. Inscrivez-vous pour synchroniser les abonnements sur tous les appareils.