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By Matthias Müller, Head of Embodied AI Lab at Intel Labs Matthias Mueller leads the Embodied AI Lab at Intel Labs, where he and his team develop algorithms pertaining to the fields of robotics, computer vision, graphics, and machine learning. The goal of Intel Labs’ Embodied AI Lab is to build inte

Swedish unicorn Neo4j counts the likes of Adobe, AstraZeneca, Comcast, Ebay, and UBS among its growing clientele.

Sometime I become so addicted to a particular framework that I completely forget about how the world is changing out there. Therefore…

Papers With Code highlights trending Machine Learning research and the code to implement it.

Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph algorithms it is...

The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this...

Let’s understand how to optimize GRL models and their ability to incorporate different network features

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Can GraphQL scale beyond workgroups or departments? Changes to Apollo GraphQL's federation gateway should make GraphQL better suited for enterprise scale adoption.

My keynote at CIKM 2021 workshop on Knowledge Injection in Neural Networks--about uses/misuses of #LLMs, importance of explicit knowledge, and our ongoing...

The AstraZeneca Biological Insights Knowledge Graph (BIKG) system design paper from our team is out on BioArxiv. 🥳🥳 The draft: https://lnkd.in/dQKddMs8...

Classical event logs have the fundamental shortcoming of describing process behavior only in isolated process executions from the viewpoint of a single case entity. Most real-life processes involve…

Excited to share our collaboration with @GoogleAI: SMORE is a scalable knowledge graph completion and multi-hop reasoning system that scales to hundreds of millions of entities and relations. @ren_hongyu, @hanjundai, et al.https://t.co/O2xM9iYMjihttps://t.co/BImVmbZAFi pic.twitter.com/H52aFOejkv— Jure Leskovec (@jure) November 1, 2021

SpikeX: spaCy Pipes for Knowledge Extraction 🧶 WikiPageX links Wikipedia pages to chunks in text 💎 ClusterX picks noun chunks in a text and clusters... 13 comments on LinkedIn

Taxonomies are useful for managing and analyzing data, and not just content.

Let me tell you about ✨graph cellular automata✨ and why I am so excited about them: 1. Decentralized / emergent computation on graphs is a fundamental...

Our work on GNNs for inductive link prediction got the best paper award at International Semantic Web Conference 2021! Wouldn't be possible without the...

Do you want to know what’s in store for the future of AI? Catch these deep learning experts in a panel led by our own Gadi Singer. Panelists Gary Marcus...

Learn how to combine OCR, named entity linking, relation extraction and external enrichment databases to construct a biomedical knowledge…

🎉 We are excited to announce the first release of SkillNer today (check the demo: bit.ly/3pA8CRg). Skillner is the first  open-source library to extract... 10 comments on LinkedIn

We know a lot more about how AlphaFold 2 works, but the mystery of why proteins fold the way they do remains something of a mystery.

A new class of GNNs! This Tuesday in the #graph reading group, James Rowbottom and Ben Chamberlain present their "GRAND: Graph Neural Diffusion" paper ...

Amazon.com: Knowledge Graphs and Big Data Processing (Lecture Notes in Computer Science Book 12072) eBook : Janev, Valentina, Graux, Damien, Jabeen, Hajira, Sallinger, Emanuel: Kindle Store

Joins play a pretty important role for defining the semantics of evaluating SPARQL queries even though they are not a part of the SPARQL syntax. One does not have to think about joins as long as their queries are restricted to basic graph patterns. However once more complex constructs appear in the query, their results are typically combined using the good old relational join operator. It has certain quirks, for example, in how it deals with nulls, and it's important to understand those to avoid result explosion and performance problems.

This week’s publication of the Pandora Papers--which the International Consortium of International Journalists based on a trove of private data leaked

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reason…

Distribution Knowledge Embedding for Graph Pooling https://t.co/YsQBXwbFtZ pic.twitter.com/ZXbpOKDrLC— Aaron Bradley (@aaranged) September 30, 2021

Super nice talk by @matej_zecevic on #Neuro-#Causality and our integration of graph neural networks and structural causal models. 🎞️👉 https://t.co/S2XNuOqZ61 🙏 to @JackccLu for inviting Matej! pic.twitter.com/jamZ20WoGt— Kristian Kersting (@kerstingAIML) September 28, 2021

Bob Kasenchak discusses the uses and abuses of polyhierarchy. It is not a license to play fast and loose with the All-Some Rule.