Library for deep learning on graphs

Relational Graph Convolutional Networks: A Closer Look https://t.co/JOg3a2YtUV pic.twitter.com/XZmYnRAVxY— Aaron Bradley (@aaranged) July 22, 2021

Some of the handiest tools in an ML engineer’s toolbelt are vector embeddings, a way of representing data in a dense vector space. An early example of the

Zero-shot Visual Question Answering using Knowledge Graph https://t.co/R9VNiLFdZE pic.twitter.com/8TbY3lkgGu— Aaron Bradley (@aaranged) July 13, 2021

Learn about a Google patent that enables searchers to explore connections between related entities in different types of media.

SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks https://t.co/XZSHPF44Hj pic.twitter.com/p9MOGGxhB1— Aaron Bradley (@aaranged) July 6, 2021

Wouldn't it be great if AI could reason with commonsense knowledge?Check out our latest blog post on a new question answering model, QA-GNN, that jointly reasons with language models and knowledge graphs, by @michiyasunaga!https://t.co/6b6e7ZWCba— Stanford AI Lab (@StanfordAILab) July 13, 2021

An ontology for the formalization and visualization of scientific knowledge https://t.co/ZWx7UUwyzk pic.twitter.com/ATQlJfXoHw— Aaron Bradley (@aaranged) July 12, 2021

In this work we propose a new approach for semantic web matching to improve the performance of Web Service replacement. Because in automatic systems we should ensure the self-healing,...

Geometric Deep Learning on Molecular Representations Atz et al.: https://lnkd.in/dEeydSb #ChemicalPhysics #ArtificialIntelligence #Biomolecules...

As a data scientist or machine learning engineer it's not easy to keep up with the latest research. Zeta Alpha the next generation neural discovery platform...

Graph Kernel Attention Transformers: Toward Expressive and Scalable Graph Processing | Synced...

Common sense knowledge graph embedding projected fasttext document embeddings vs transformers In these two blog posts, Radix demonstates deep learning...

Learning Attributed Graph Representations with Communicative Message Passing Transformer • Constructing appropriate representations of molecules lies at...

The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics...

As organizer of the WAIC European Online Forum, Expand.hk would like to thank six distinguished speakers:  Gadi Singer, Walid Saba, Dr. Keith Duggar, ...

Graph Neural Networks User Group

In this article, we introduce Deep Learning Graphs and go through 7 up-and-coming open-source libraries for graph deep learning, ranked in order of ...

Interested in explainability for graph neural networks? Check out this list created by our team in AstraZeneca (Gavin E. and Sebastian Nilsson)! https:...

Check out all the latest stories, product features and special promo offers from Careem.

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent...

Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.

Mapped, which simplifies access to building assets via a digital twin API, raised $6.5 million in a seed II round.

Our e-book aims to shed light on what we believe is a real game-changer for those looking to improve upon simplistic answers sometimes arrived at by using traditional ML algorithms and approaches. We show how you are able to combine the power of both graphs and ML (in a variety of different ways) to help you arrive at better answers compared to using standard ML approaches alone.

With data scientists in high demand, NASA is pursuing a pioneering approach to filling its skills gaps.

"Very Deep Graph Neural Networks Via Noise Regularisation" by Petar Veličković, Yulia Rubanova, Alvaro Sanchez Gonzalez, Jonathan Godwin, et al. Paper...

Iconology is a branch of art history that investigates the meaning of artworks in relation to their social and cultural background. Nowadays, several interdisciplinary research fields leverage...

We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of...

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