Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered...

This series summarizes a comprehensive taxonomy for machine learning on graphs and reports details on GraphEDM (Chami et. al), a new framework for unifying different learning approaches Graphs are…

Directional Graph Networks Beaini et al.: https://lnkd.in/dKe_q3K #GraphNeuralNetworks #DirectionalGraphNetworks #ArtificialIntelligence...

Hybrid AI is more than enriching existing AI models with specialized knowledge! Find out what that means exactly and how companies can benefit from it in this article.

Description of the Problem: Artificial intelligence (AI) in health has shown significant success by recognizing patterns in ECG to predict atrial fibrillation, hypertrophic cardiomyopathy, and cardiac arrest. Further, investigating brain MRIs for tumors, or mining patterns in fMRIs to estimate chanc

Proud to share our 150-page "proto-book" with @mmbronstein @joanbruna @TacoCohen on geometric DL! Through the lens of symmetries and invariances, we attempt to distill "all you need to build the architectures that are all you need". https://t.co/CBN0IG8BXR More info below! 🧵 https://t.co/NBwmeuR1vV

How simple structured data trumps clever machine learning

SpikeX - SpaCy Pipes for Knowledge Extraction • A collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction...

This week SambaNova landed $676 million in Series D funding, making it the best-funded AI startup in the world. For many of my colleges…

Start with Graph Neural Networks from zero and implement a graph convolutional layer in Pytorch

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering - The problem of answering questions using knowledge from pre-trained...

On the role of knowledge that is structured, explicit and intelligible in providing a path toward higher machine intelligence

Apollo GraphQL has updated its Apollo Federation offering, making it more accessible to a wider range of developers using different tools.

AUSTIN, TX – April 06, 2021 – Katana Graph, a high performance scale-out graph processing, AI and analytics company, announced today that it has optimized its graph engine for the new 3rd Gen Intel Xeon Scalable processor and memory systems. Katana Graph can now take advantage of the latest generation Intel Xeon Scalable processors and […]

Wrapping up the 2021 edition of the Graph Deep Learning course at USI Università della Svizzera italiana, we had an amazing guest lecture by Prof Kyle ...

Rich Text, Lean Knowledge Bases: Knowledge Extraction from Financial Documents - my keynote presentation at the AAAI-KDF now available as recording, in...

I've written a blog which shows you how to embed a #3D #MindMap with hyperlinks in your homepage https://t.co/Y1ZUUreAio , an example is available at https://t.co/l8ap24ZcMc pic.twitter.com/5QWesXLaFt— Ingo Straub (@inforapid) March 14, 2021

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

📣 The code & datasets of our #CVPR2021 paper on #neurosymbolic explanatory interactive learning (NeSyXIL) is out!It involves a novel object-based neural concept learner & allows one to tell a DNN to not use "any gray box" to justify its classification.https://t.co/FcGg2LTs9z pic.twitter.com/IZt8622817— Kristian Kersting (@kerstingAIML) March 16, 2021

Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts https://t.co/tJzTg12Et8 pic.twitter.com/y7uEGwQbs7— Aaron Bradley (@aaranged) March 16, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021

"Mention-centered Graph Neural Network for Document-level Relation Extraction" discovering relations between entities across a whole document using #Wikidata.(Pan et al, 2021)https://t.co/UInDj2b1gH pic.twitter.com/OZk79w9mO5— WikiResearch (@WikiResearch) March 22, 2021

"Directional Graph Networks" by Gabriele Corso, Saro Passaro, Dominique Beaini, Vincent Létourneau et al. Paper: https://lnkd.in/dpYeTiP #GraphNeuralNetworks...

Nice machine learning read of the week: E(n) Equivariant Graph Neural Networks. The paper: https://lnkd.in/dfZ3Gh4 Source code: https://lnkd.in/dNfzQRj...

Somesh Mohapatra strikes again with some representation learning for biopolymers, this time glycans! Alongside Joyce An, we propose a graph representation...

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or...

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

Library for deep learning on graphs

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In...

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption,...