graph – Page 4 – Andrew Fairless, Ph.D.

What I Read: automatic differentiation with graphs

By Andrew Fairless on January 30, 2021January 5, 2021

https://ai.facebook.com/blog/a-new-open-source-framework-for-automatic-differentiation-with-graphs A new open source framework for automatic differentiation with graphsOctober 8th, 2020 “Just as PyTorch provides a framework for automatic differentiation with tensors, GTN provides such a framework forContinue readingWhat I Read: automatic differentiation with graphs

What I Read: Traffic prediction with Graph Neural Networks

By Andrew Fairless on January 23, 2021December 30, 2020

https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks Traffic prediction with advanced Graph Neural Networks “We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum… the most successful approach to this problemContinue readingWhat I Read: Traffic prediction with Graph Neural Networks

What I Read: Symbolic Models from Deep Learning

By Andrew Fairless on January 4, 2021December 22, 2020

https://astroautomata.com/paper/symbolic-neural-nets/ Discovering Symbolic Models from Deep Learning with Inductive BiasesPaper Code Interactive NotebookPaper authors: Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho “…when doesContinue readingWhat I Read: Symbolic Models from Deep Learning

What I Read: Differentiable Reasoning over Text

By Andrew Fairless on January 1, 2021December 22, 2020

https://blog.ml.cmu.edu/2020/05/15/differentiable-reasoning-over-text/ Differentiable Reasoning over TextAuthorsBhuwan DhingraPublishedMay 15, 2020 “Modern search engines not only retrieve a list of pages relevant to a query but often try to directly answer our questionsContinue readingWhat I Read: Differentiable Reasoning over Text

What I Read: Learning Neural Causal Models

By Andrew Fairless on December 30, 2020December 20, 2020

https://arxiv.org/abs/1910.01075Learning Neural Causal Models from Unknown InterventionsNan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio “We present a new framework for meta-learning causal modelsContinue readingWhat I Read: Learning Neural Causal Models

What I Read: Guide to Graph Neural Networks

By Andrew Fairless on December 20, 2020December 8, 2020

https://medium.com/dair-ai/an-illustrated-guide-to-graph-neural-networks-d5564a551783 An Illustrated Guide to Graph Neural NetworksA breakdown of the inner workings of GNNs…Rishabh AnandMar 30 “Here, I’ll cover the basics of a simple Graph Neural Network (GNN) andContinue readingWhat I Read: Guide to Graph Neural Networks

What I Read: Transformers Graph Neural Networks

By Andrew Fairless on December 17, 2020December 4, 2020

https://graphdeeplearning.github.io/post/transformers-are-gnns/ Transformers are Graph Neural NetworksChaitanya JoshiFeb 12, 2020 “Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behindContinue readingWhat I Read: Transformers Graph Neural Networks

What I Read: Neural State Machine

By Andrew Fairless on December 16, 2020December 4, 2020

https://arxiv.org/abs/1907.03950 Learning by Abstraction: The Neural State MachineDrew A. Hudson, Christopher D. Manning “We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic viewsContinue readingWhat I Read: Neural State Machine

What I Read: scalable graph machine learning

By Andrew Fairless on December 3, 2020November 28, 2020

https://www.kdnuggets.com/2019/12/scalable-graph-machine-learning.html Scalable graph machine learning: a mountain we can climb?By Kevin Jung, Software Engineer at CSIRO Data61 “In this article, we will take a closer look at scalability for graphContinue readingWhat I Read: scalable graph machine learning

Tag: graph