Nodes & Edges @NodesEdges
Machine Learning on #Graphs // Latest #GNN Research // #ML Tips & Tricks // Signal in Connections nande.ai London, UK Joined February 2022-
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V helpful writeup on useful concepts for understanding GNNs
V helpful writeup on useful concepts for understanding GNNs
Gstp2Vec: A graph-based representation of spatiotemporal trajectories and point-of-interest #data for travel activity type identification that outperforms state-of-the-art classification models #Research #EmergingTech #MachineLearning #AI #dataScience nature.com/articles/s4159…
Absolutely love this!
Glad to share the *Long Range Graph Benchmark* - to evaluate #GNNs with long-range modeling. LRGB will be presented at #NeurIPS2022 Datasets and Benchmarks Track and was developed with an awesome collab @rampasek @michael_galkin @dom_beaini A Parviz, G Wolf and AT Luu.
Glad to share the *Long Range Graph Benchmark* - to evaluate #GNNs with long-range modeling. LRGB will be presented at #NeurIPS2022 Datasets and Benchmarks Track and was developed with an awesome collab @rampasek @michael_galkin @dom_beaini A Parviz, G Wolf and AT Luu.
Do you know what symmetries your neural network learn? In our #NeurIPS2022 paper, we introduce LieGG - a method to retrieve generators of symmetry groups learned by a neural network from the data. “LieGG: Studying Learned Lie group Generators” arxiv.org/abs/2210.04345 1/5🧵
New PhD project on geometric DL for spatiotemporal data in Amsterdam by @egavves! (I will serve as industry co-supervisor) The project is quite open ended, so lots of room for your input. Great opportunity to work in an exciting area with top-notch colleagues in the QUVA lab.
New PhD project on geometric DL for spatiotemporal data in Amsterdam by @egavves! (I will serve as industry co-supervisor) The project is quite open ended, so lots of room for your input. Great opportunity to work in an exciting area with top-notch colleagues in the QUVA lab.
I remember @PetarV_93 saying graphs are everywhere and not quite understanding what he meant. Now it all makes sense
Great resource for both those wanting to learn more about GNNs
I'm happy to share our recent work on Gradient Gating (G^2), a simple but very powerful way of gating the node-wise update of message-passing GNNs. paper: arxiv.org/abs/2210.00513 code: github.com/tk-rusch/gradi… w/ @b_p_chamberlain, M. Mahoney, @mmbronstein, S. Mishra 1/7
👀👀👀
We’re pleased to introduce Make-A-Video, our latest in #GenerativeAI research! With just a few words, this state-of-the-art AI system generates high-quality videos from text prompts. Have an idea you want to see? Reply w/ your prompt using #MetaAI and we’ll share more results.
New work on Crystal Material Property Prediction using transformer, accepted to NeurIPS 2022.
New work on Crystal Material Property Prediction using transformer, accepted to NeurIPS 2022.
Happening now!
Two exciting announcements this morning -- at Kumo.AI we've just closed our Series B round (led by Sequoia) and we are rolling out our enterprise AI platform on October 16! techcrunch.com/2022/09/27/kum…
Improving GNNs with "power embed" normalization
Don't miss out! Starting in 2 days
The best GNNs for molecular property prediction from 3D structures usually don't use pre-training. Why not? We show that pre-training via denoising structures gives SOTA results. Paper📄: arxiv.org/abs/2206.00133 Code💻: bit.ly/3DzkKZO Poster🖼️: bit.ly/3BqyuU5
🤯Floored by receiving more than 250 submissions for the "Learning on Graphs" Conference! @LogConference 🖼️Look at this exciting word cloud of all paper titles...can you spot *your* favourite topic in there?
Please spread the word that the @LogConference abstract submission deadline is on Sept 9th! Submit your papers! logconference.org/cfp/ We ensure excellent reviewers for both the 9 page full paper track (in PMLR proceedings if accepted) and the 4 page extended abstract track 👌
Here is last week's video of @Francesco_dgv, @_JRowbottom and @b_p_chamberlain presenting their paper "Graph Neural Networks as Gradient Flows"! youtu.be/sgTTtmwOMgE
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