graph – Andrew Fairless, Ph.D.

What I Read: Data Flywheels, LLM

By Andrew Fairless on October 17, 2024July 20, 2024

https://www.sh-reya.com/blog/ai-engineering-flywheel Data Flywheels for LLM ApplicationsShreya ShankarJul 1, 2024 “This diagram illustrates my (idealized) architecture of an LLM pipeline, from input processing through evaluation and logging. It showcases ideas I’llContinue readingWhat I Read: Data Flywheels, LLM

What I Read: Chatbots Understand Text

By Andrew Fairless on March 18, 2024February 5, 2024

https://www.quantamagazine.org/new-theory-suggests-chatbots-can-understand-text-20240122/ New Theory Suggests Chatbots Can Understand TextAnil Ananthaswamy1/22/24 “Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.”

What I Read: Neural algorithmic reasoning

By Andrew Fairless on January 9, 2024November 7, 2023

https://thegradient.pub/neural-algorithmic-reasoning/ Neural algorithmic reasoningPetar Veličković14.Oct.2023 “…we will talk about classical computation… Think shortest path-finding, sorting, clever ways to break problems down… we will also investigate how to capture such computationContinue readingWhat I Read: Neural algorithmic reasoning

What I Read: SAT Solvers

By Andrew Fairless on January 8, 2024November 7, 2023

https://www.borealisai.com/research-blogs/tutorial-9-sat-solvers-i-introduction-and-applications/ Tutorial #9: SAT Solvers I: Introduction and applicationsS. PrinceNov. 10, 2020 “This tutorial concerns the Boolean satisfiability or SAT problem…. we’ll show how to fit both neural networks andContinue readingWhat I Read: SAT Solvers

What I Read: Bonsai Networks, RNNs

By Andrew Fairless on October 3, 2023September 4, 2023

https://cprimozic.net/blog/growing-sparse-computational-graphs-with-rnns/ Growing Bonsai Networks with RNNsCasey Primozic “This writeup introduces what I’m calling Bonsai Networks – extremely sparse computational graphs produced by training and pruning RNNs. They provide an interpretableContinue readingWhat I Read: Bonsai Networks, RNNs

What I Read: Natural Language, supply chains

By Andrew Fairless on July 5, 2023May 31, 2023

https://datasciencecampus.ons.gov.uk/using-natural-language-processing-for-the-analysis-of-global-supply-chains/ Using Natural Language Processing for the analysis of global supply chainsIoannis Kaloskampis Bernard Peat Charles McGowan Paige Hunter Melissa Bui David BradnumMay 11, 2023 “…we explored whether cutting-edge dataContinue readingWhat I Read: Natural Language, supply chains

What I Read: Graph Neural Networks

By Andrew Fairless on May 17, 2023March 30, 2023

https://www.assemblyai.com/blog/ai-trends-graph-neural-networks/ AI trends in 2023: Graph Neural NetworksMarco RamponiDeveloper Educator at AssemblyAIMar 21, 2023 “In the last couple of years Graph Neural Networks have quietly become the dark horse behindContinue readingWhat I Read: Graph Neural Networks

What I Read: Geometric Deep Learning

By Andrew Fairless on April 10, 2023February 22, 2023

https://thegradient.pub/towards-geometric-deep-learning/ Towards Geometric Deep LearningMichael Bronstein18.Feb.2023 “Geometric Deep Learning is an umbrella term for approaches considering a broad class of ML problems from the perspectives of symmetry and invariance.”

What I Read: Beyond Message Passing, Graph Neural Networks

By Andrew Fairless on June 6, 2022May 23, 2022

https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman/ Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural NetworksMichael Bronstein07.May.2022 “…the “node and edge-centric” mindset of current graph deep learning schemes imposes strong limitations… we propose physics-inspired “continuous”Continue readingWhat I Read: Beyond Message Passing, Graph Neural Networks

What I Read: Graph ML, missing node features

By Andrew Fairless on May 16, 2022April 25, 2022

https://blog.twitter.com/engineering/en_us/topics/insights/2022/graph-machine-learning-with-missing-node-features Graph machine learning with missing node featuresEmanuele RossiMaria GorinovaThursday, 17 March 2022 “…GNNs typically run under the assumption of a full set of features available for all nodes. InContinue readingWhat I Read: Graph ML, missing node features

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