graph – Page 2 – Andrew Fairless, Ph.D.

What I Read: Engineering Trade-Offs in Automatic Differentiation

By Andrew Fairless on March 8, 2022January 20, 2022

https://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/ Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and JuliaDecember 25 2021Author: Christopher Rackauckas “To understand the differences between automatic differentiation libraries, let’s talk about theContinue readingWhat I Read: Engineering Trade-Offs in Automatic Differentiation

What I Read: AI Accelerators, Architectural Foundations

By Andrew Fairless on March 1, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-iii-architectural-foundations-3f1f73d61f1f AI Accelerators — Part III: Architectural FoundationsAdi FuchsDec 5 “Many different companies build AI accelerators. The landscape is filled with an abundance of ideas and assumptions based on theContinue readingWhat I Read: AI Accelerators, Architectural Foundations

What I Read: AI Accelerators

By Andrew Fairless on February 28, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-ii-transistors-and-pizza-or-why-do-we-need-accelerators-75738642fdaa AI Accelerators — Part II: Transistors and Pizza (or: Why Do We Need Accelerators)?Adi FuchsDec 5 “There can not be a “one size fits all” approach when it comesContinue readingWhat I Read: AI Accelerators

What I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

By Andrew Fairless on January 3, 2022December 11, 2021

https://towardsdatascience.com/graph-neural-networks-through-the-lens-of-differential-geometry-and-algebraic-topology-3a7c3c22d5f Graph Neural Networks through the lens of Differential Geometry and Algebraic TopologyMichael Bronstein “Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning… tools fromContinue readingWhat I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

What I Read: Understanding Convolutions on Graphs

By Andrew Fairless on September 29, 2021September 21, 2021

https://distill.pub/2021/understanding-gnns/ Understanding Convolutions on GraphsUnderstanding the building blocks and design choices of graph neural networks.Ameya DaigavaneBalaraman RavindranGaurav AggarwalGoogle ResearchSept. 2, 202110.23915/distill.00032 “Graph neural networks (GNNs) are a family of neuralContinue readingWhat I Read: Understanding Convolutions on Graphs

What I Read: Introduction to Graph Neural Networks

By Andrew Fairless on September 28, 2021September 21, 2021

https://distill.pub/2021/gnn-intro/ A Gentle Introduction to Graph Neural NetworksBenjamin Sanchez-LengelingEmily ReifAdam PearceAlex WiltschkoGoogle ResearchSept. 2, 202110.23915/distill.00033 “Neural networks have been adapted to leverage the structure and properties of graphs. We exploreContinue readingWhat I Read: Introduction to Graph Neural Networks

What I Read: Graph Theory Into New Dimensions

By Andrew Fairless on September 16, 2021September 4, 2021

https://www.quantamagazine.org/how-big-data-carried-graph-theory-into-new-dimensions-20210819/ How Big Data Carried Graph Theory Into New DimensionsStephen OrnesContributing WriterAugust 19, 2021 ““Now we know that the network is just the shadow of the thing,”… If a dataContinue readingWhat I Read: Graph Theory Into New Dimensions

What I Read: Against SQL

By Andrew Fairless on September 1, 2021August 17, 2021

https://scattered-thoughts.net/writing/against-sql/ Against SQLJamie BrandonLast updated 2021-07-09 “The core message that I want people to take away is that there is potentially a huge amount of value to be unlocked byContinue readingWhat I Read: Against SQL

What I Read: demystifying graph deep learning

By Andrew Fairless on August 30, 2021August 17, 2021

https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/ An attempt at demystifying graph deep learningEric Ma “There are a ton of great explainers of what graph neural networks are. However, I find that a lot of themContinue readingWhat I Read: demystifying graph deep learning

What I Read: Geometric, Deep Learning

By Andrew Fairless on August 16, 2021July 22, 2021

https://towardsdatascience.com/geometric-foundations-of-deep-learning-94cdd45b451d?gi=d7fa7307aa63 Geometric foundations of Deep LearningGeometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principlesContinue readingWhat I Read: Geometric, Deep Learning

Tag: graph