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GML Express: keynotes at ICLR, topics at ICML 2021, and new GNN tutorials.
"There are 3 ways to make a living: be first, be smarter, or cheat." Margin Call
Hello and welcome to the graph ML newsletter!
This is an Express version ⚡ of the newsletter that highlights recent events in graph machine learning. As ICLR just took place 💻, ICML released their accepted papers 📝, and NeurIPS started reviewing process ⚖, it’s good time to look back at what has happened in the world of graphs 🌐
This year's International Conference on Learning Representations (ICLR) was supposed to be in Vienna, but the second year for obvious reasons it happened online. I personally liked more 2020 version of ICLR, maybe because back then online conferences just started and I was not exhausted by the zoom meetings. But on the positive side, there were gather.town poster sessions which remind real connections between attendees.
The compilation of keynotes was amazing: Michael Bronstein’s talk about geometric deep learning, Timnit Gebru’s talk on fairness in AI, Alexei Efros’s talk on various forms of self-supervised learning, among others. Top content 👏 Besides, you can browse the short videos for more than 800 ICLR papers, many of which are on graph neural networks and graph embeddings.
ICML 2021 is coming this July and the paper titles are now available. ICML 2021 has a slight increase in submissions and accepted papers compared to 2020 and retains the same acceptance rate. Many stats about ICML 2021 can be found in this tweet:
There are about 60 graph papers, on topics such as oversmoothing, explainability, expressivity, robustness, and so on (categorization is offered here). Top authors at ICML 2021 who publish graph papers are displayed below:
North American ACL conference accepted 477 papers that are centered around NLP applications. There are about 30 papers that apply graphs to text summarization, dialogue systems, translation, and more. Additionally, there is an interesting tutorial “Deep Learning on Graphs for Natural Language Processing”, which is based on Graph4NLP library.
MLSys is one of the conferences that look at the intersection between ML and industrial systems and this year there was a spectacular workshop on Graph Neural Networks and Systems (GNNSys'21). The talks are available online and include topics such as GNNs on graphcore's IPU, chip placement optimization, particle reconstruction at the large hadron collider and more.
A legendary Stanford CS224W course on graph ML now releases videos on YouTube. There are 20 video lectures and slides available online. Topics include PageRank, GNNs, knowledge graphs, generative models, community detection, and applications.
GNN User Group
GNN user group organized by AWS and Nvidia continues to host monthly talks on the latest research in the GNN world. Recent talks are about graph transformers, GBDTs for heterogeneous graphs, and GNNs in therapeutics. Check them out here.
Pytorch Geometric tutorials
A nice channel that goes over the implementation of GNN models in PyTorch-Geometric. It also features talks and presentations from researchers working in the field. There are more than 15 tutorials available.
Blog posts, competitions, and books
Knowledge Graphs @ ICLR 2021
One and only Michael Galkin does it again with a superior digest of knowledge graph research at ICLR 2021. Topics include reasoning, temporal logics, and complex question-answering in KGs: a lot of novel ideas and less SOTA-chasing work!
Graphs at ICLR 2021
Another take on graph works at ICLR by Daniele Paliotta. He highlights some papers that solve overmoothing, over-squashing, heterophily, and attention problems in GNNs.
Graph Neural Networking Challenge 2021
If you still have energy after OGB-LSC challenge, there is a new competition, organized by Technical University of Catalonia (UPC) and ITU, about building GNNs to predict source-destination routing time. The goal is to test the generalization abilities of GNNs: training on small graphs and testing on much larger graphs.
Geometric Deep Learning Book
A new book by graph ML experts Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković on geometric deep learning is released. 156 pages on exploring symmetries that unify different ML neural network architectures. An accompanying post nicely introduces the history of geometry and its impact on physics. It's exciting to see a categorization of many ML approaches from the perspective of the group theory.