What Is Graph Analytics?. Basic Introduction to what Graph… | by Aasavari Kaley | The Startup | Feb, 2021 | Medium
Basic Introduction to what Graph Analytics is…
GitHub - thunlp/GNNPapers: Must-read papers on graph neural networks (GNN)
Must-read papers on graph neural networks (GNN). Contribute to thunlp/GNNPapers development by creating an account on GitHub.
[2102.11965] Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of...
Recommendations using graph neural networks - Recommender systems
personalized recommendations #8
Graph Convolutional Embeddings for Recommender Systems
"Graph Convolutional Embeddings for Recommender Systems" by Paula Gómez Duran, Alexandros Karatzoglou et al. Paper: https://lnkd.in/dHskknV #graphnn #...
Graph Convolutional Networks —Deep Learning on Graphs | by Francesco Casalegno | Jan, 2021 | Towards Data Science
Graph applications are ubiquitous, learn how to use Machine Learning with them!
how we can get to GNNs starting from conventional CNNs and changing a few assumptions
A few days ago I gave a lecture on Graph Neural Networks for the new course on "Graph Deep Learning" that my group is teaching at USI Università della ...
Introducing Persistent Message Passing (PMP)! We endow GNNs 🕸️ with an explicit, persistent, memory 💾 of their past computations.
Introducing Persistent Message Passing (PMP)! We endow GNNs 🕸️ with an explicit, persistent, memory 💾 of their past computations. with Heiko Strathmann...
We have a new paper showing that #GraphNeuralNetworks are state-of-the-art for representations for #ElectronicHealthRecords data!!
We have a new paper showing that #GraphNeuralNetworks are state-of-the-art for representations for #ElectronicHealthRecords data!! EHR data is inherently... 46 comments on LinkedIn
Aleksa Gordić on LinkedIn: #graphneuralnetworks #gat #graphs | 33 comments
A couple of beautiful things happened during this week! 1. My pytorch-GAT project amassed 712 stars over the last week! Which is a beautiful signal to... 33 comments on LinkedIn
Vincent Boucher on LinkedIn: #MachineLearning #ChemicalPhysics #GraphNeuralNetworks
MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks Wang et al.: https://lnkd.in/gs2g2mk #MachineLearning #ChemicalPhysics...
model the interactions between drugs and various biological targets in the body to predict which ones could be used for the treatment of Covid-19. I framed the problem as edge regression on a bipartite graph, and used a variant of graph convolutional networks to predict over 50 potential Covid-19 treatments.
I'm so excited to share this! 🚀✨ My latest research project was to model the interactions between drugs and various biological targets in the body to... 15 comments on LinkedIn
Graph Convolutional Networks (GCNs) combine deep learning with feature diffusion to produce useful node embeddings
Graph Convolutional Networks (GCNs) combine deep learning with feature diffusion to produce useful node embeddings. The embeddings are constructed by taking...
Petar Veličković on LinkedIn: Theoretical Foundations of Graph Neural Networks
The recording of my talk on Theoretical Foundations of Graph Neural Networks is now live (+ slides are in the description)! 🕸️ Join me as I derive GNNs...
Pavan Kapanipathi on Twitter: "3/5 [Demo]: Neuro-Symbolic Question Answering: A KBQA system that enables use of neuro-symbolic reasoner by leveraging semantic parsing. SoTA on two DBpedia based KBQA datasets Paper: https://t.co/4p0Sy4vlGw Blog: https://t.co/mYIXuH8dbA @ibrahimabdelazz @sroukos" / Twitter
3/5 [Demo]: Neuro-Symbolic Question Answering: A KBQA system that enables use of neuro-symbolic reasoner by leveraging semantic parsing. SoTA on two DBpedia based KBQA datasets Paper: https://t.co/4p0Sy4vlGwBlog: https://t.co/mYIXuH8dbA@ibrahimabdelazz @sroukos— Pavan Kapanipathi (@pavankaps) February 6, 2021
Vincent Boucher on LinkedIn: #ArtificialIntelligence #DeepLearning #ReinforcementLearning
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning Jiang et al.: https://lnkd.in/dSSsm-d #ArtificialIntelligence ...
Artificial Intelligence needs to be more context-aware
Artificial Intelligence (AI) has made enormous strides in the last several years both due to the advent of technologies such as deep…
WikiResearch on Twitter
"CHOLAN: A Modular Approach for Neural Entity Linking on @Wikipedia and @Wikidata", a pipeline of two transformer-based models integrated sequentially for end-to-end entity linking.(Kannan Ravi et al, 2021)paper: https://t.co/VD9uyxc0RScode/data: https://t.co/BZYG5XGPO5 pic.twitter.com/aqLPDopXAb— WikiResearch (@WikiResearch) February 8, 2021
Aaron Bradley on Twitter
A Framework for Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments https://t.co/oGdCVjTCyv pic.twitter.com/fL2bmDIgN4— Aaron Bradley (@aaranged) February 9, 2021
How to get started with Graph Machine Learning
Deep learning update: What have I learned about Graph ML in 2 months?
WikiResearch on Twitter
"Towards a Systematic Approach to Sync FactualData across #Wikipedia, #Wikidata and ExternalData Sources"(Hellmann et al, 2021)paper: https://t.co/TlYkPg5jg6tool: https://t.co/YPVhcgP8W7 pic.twitter.com/bSnkPOmHeU— WikiResearch (@WikiResearch) February 1, 2021
Graphs & Networks on Twitter
Graph analysis using the tidyverse.https://t.co/PIIN0UFlzH #rstats #graphs pic.twitter.com/cs7ynHD9rh— Graphs & Networks (@TheOrbifold) February 2, 2021
Aaron Bradley on Twitter
.@tb_tomaz shows "how you can create a news monitoring data pipeline that combines Natural Language Processing and knowledge graphs technologies." https://t.co/VHYOKOi95L— Aaron Bradley (@aaranged) February 2, 2021
The Semantic Hypergraph — Graphbrain 0.4.0 documentation
Giuseppe Futia, PhD on LinkedIn: Nexa Lunch Seminar - eXplainable AI
Inoreader - Take back control of your news feed
One place to keep up with all your information sources. With Inoreader, content comes to you, the minute it's available. Subscribe to RSS Feeds, Blogs, Podcasts, Twitter searches, Facebook pages, even Email Newsletters! Get unfiltered news feeds or filter them to your liking.
Machine Intelligence Research Directions in 2021
Graphcore Director of Research Carlo Luschi discusses AI research directions in 2021, including training efficiency and parallel training.
Buckets and Balls
Linked Data is still largely unknown, or misunderstood and undervalued. Often, people find it simply too difficult. So I keep looking for new ways to make
Giuseppe Futia, PhD on LinkedIn: #NumPy #gat #graphs
Check out my new article for Towards Data Science: "Graph Attention Networks Under the Hood". A Step-by-step Guide From Math to #NumPy https://lnkd.in...
Interactive and Interpretable Machine Learning and Graph-Based Data Science
Sat, Jan 30, 2021, 11:30 AM: Hi Everyone,Agenda:11:30 am - 11:45 Introductions/Meet and greet11:45 - 12:45: Interactive Visualization for Interpretable and Interactive Machine Learning with Professor