2022 – Page 14 – Andrew Fairless, Ph.D.

What I Read: How to Train Decision-Making AIs

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

https://thegradient.pub/how-to-train-your-decision-making-ais/ How to Train your Decision-Making AIs10.Dec.2021Ruohan ZhangDhruva Bansal “How do humans transfer their knowledge and skills to artificial decision-making agents more efficiently? What kind of knowledge and skills shouldContinue readingWhat I Read: How to Train Decision-Making AIs

What I Read: What Does It Mean for AI to Understand?

By Andrew Fairless on February 7, 2022January 20, 2022

https://www.quantamagazine.org/what-does-it-mean-for-ai-to-understand-20211216/ What Does It Mean for AI to Understand?Melanie MitchellContributing ColumnistDecember 16, 2021 “It’s simple enough for AI to seem to comprehend data, but devising a true test of aContinue readingWhat I Read: What Does It Mean for AI to Understand?

What I Read: MLOps Documentation

By Andrew Fairless on February 2, 2022January 20, 2022

https://www.kdnuggets.com/2021/12/best-practices-mlops-docs.html Best Practices for MLOps DocumentationBy Nifesimi Ademoye “Why does technical documentation matter in MLOps?…. MLOps levels are defined by the extent of automation of processes in an ML pipeline,Continue readingWhat I Read: MLOps Documentation

What I Read: AI Researchers Fight Noise by Turning to Biology

By Andrew Fairless on February 1, 2022December 12, 2021

https://www.quantamagazine.org/ai-researchers-fight-noise-by-turning-to-biology-20211207/ AI Researchers Fight Noise by Turning to BiologyAllison WhittenContributing WriterDecember 7, 2021 “Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking toContinue readingWhat I Read: AI Researchers Fight Noise by Turning to Biology

What I Read: Semi-Supervised Learning

By Andrew Fairless on January 31, 2022December 12, 2021

https://lilianweng.github.io/lil-log/2021/12/05/semi-supervised-learning.html Learning with not Enough Data Part 1: Semi-Supervised LearningDec 5, 2021 by Lilian Weng “When facing a limited amount of labeled data for supervised learning tasks, four approaches areContinue readingWhat I Read: Semi-Supervised Learning

What I Read: Einstein Summation in Deep Learning

By Andrew Fairless on January 26, 2022December 11, 2021

https://rockt.github.io/2018/04/30/einsum Einsum is All you Need – Einstein Summation in Deep LearningTim Rocktäschel30/04/2018 – updated 02/05/2018 “If you are anything like me, you find it difficult to remember the namesContinue readingWhat I Read: Einstein Summation in Deep Learning

What I Read: How Kalman filter works

By Andrew Fairless on January 25, 2022December 11, 2021

https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/ How a Kalman filter works, in pictures “I have to tell you about the Kalman filter… Surprisingly few software engineers and scientists seem to know about it… it isContinue readingWhat I Read: How Kalman filter works

What I Read: Permutation Tests

By Andrew Fairless on January 19, 2022December 11, 2021

https://towardsdatascience.com/how-to-use-permutation-tests-bacc79f45749 A walkthrough of permutation tests and how they can be applied to time series data.Michael BerkSep 21 “In this post we will discuss the basics of permutation tests andContinue readingWhat I Read: Permutation Tests

What I Learn: Meta-Learning, Keyphrase Extraction

By Andrew Fairless on January 18, 2022December 11, 2021

https://sinequa.medium.com/meta-learning-for-keyphrase-extraction-54ec960cd2ba Meta-Learning for Keyphrase ExtractionSinequaSep 6 “KeyPhrase Extraction (KPE) is the process of extracting relevant chunks of words from a document to best capture and represent its content…. we outlineContinue readingWhat I Learn: Meta-Learning, Keyphrase Extraction

Year: 2022