2021 – Page 14 – Andrew Fairless, Ph.D.

What I Read: AI, Colon Cancer

By Andrew Fairless on May 14, 2021April 17, 2021

https://www.wired.com/story/ai-help-wipe-out-colon-cancer/ Sara HarrisonBusiness04.12.2021 07:00 AMThis AI Could Help Wipe Out Colon CancerMedtronic’s GI Genius, recently cleared by the FDA, will help doctors identify precancerous polyps. “…the Food and Drug AdministrationContinue readingWhat I Read: AI, Colon Cancer

What I Read: 3 Statistical Paradoxes

By Andrew Fairless on May 13, 2021April 17, 2021

https://towardsdatascience.com/top-3-statistical-paradoxes-in-data-science-e2dc37535d99 Top 3 Statistical Paradoxes in Data ScienceObservation bias and sub-group differences generate statistical paradoxes.Francesco Casalegno “Observation bias and sub-group differences can easily produce statistical paradoxes in any data scienceContinue readingWhat I Read: 3 Statistical Paradoxes

What I Read: Continuous Training Strategy

By Andrew Fairless on May 12, 2021April 17, 2021

https://www.superwise.ai/resources/framework-for-a-successful-continuous-training-strategy Framework for a successful Continuous Training StrategyOr Itzary & Liran NahumMarch 18, 2021 “…ML models need to be retrained regularly…. Continuous training seeks to automatically and continuously retrain theContinue readingWhat I Read: Continuous Training Strategy

What I Read: Models of Data Science teams

By Andrew Fairless on May 11, 2021April 17, 2021

https://www.marcosantoni.com/chess_vs_checkers_teams.html Marco SantoniModels of Data Science teams: Chess vs Checkers “One of the key decisions to take when building a data science team is the mix of roles…. I’ll compareContinue readingWhat I Read: Models of Data Science teams

What I Read: Reducing Toxicity in Language Models

By Andrew Fairless on May 10, 2021April 17, 2021

https://lilianweng.github.io/lil-log/2021/03/21/reducing-toxicity-in-language-models.html Reducing Toxicity in Language Modelsby Lilian WengMar 21, 2021 “Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior andContinue readingWhat I Read: Reducing Toxicity in Language Models

What I Read: Decentralized AI For Healthcare

By Andrew Fairless on May 7, 2021April 17, 2021

https://thegradient.pub/decentralized-ai-for-healthcare/ Decentralized AI For Healthcareby Zehra HayirciApril 8 2021 8:52 PM “Federated Learning… is the latest distributed learning framework that proposes training on locally stored data with local computational powerContinue readingWhat I Read: Decentralized AI For Healthcare

What I Read: Zero-Shot Learning

By Andrew Fairless on May 6, 2021April 13, 2021

https://www.kdnuggets.com/2021/04/zero-shot-learning.html Zero-Shot Learning: Can you classify an object without seeing it before?Developing machine learning models that can perform predictive functions on data it has never seen before has become anContinue readingWhat I Read: Zero-Shot Learning

What I Read: Weight Banding

By Andrew Fairless on May 5, 2021April 10, 2021

https://distill.pub/2020/circuits/weight-banding/ Weight BandingMichael PetrovChelsea VossLudwig SchubertNick CammarataGabriel GohChris OlahApril 8, 2021DOI 10.23915/distill.00024.009 “Open up any ImageNet conv net and look at the weights in the last layer. You’ll find aContinue readingWhat I Read: Weight Banding

What I Read: Branch Specialization

By Andrew Fairless on May 4, 2021April 10, 2021

https://distill.pub/2020/circuits/branch-specialization/ Branch SpecializationChelsea VossGabriel GohNick CammarataMichael PetrovLudwig SchubertChris OlahApril 5, 2021DOI 10.23915/distill.00024.008 “Branch specialization occurs when neural network layers are split up into branches. The neurons and circuits tend toContinue readingWhat I Read: Branch Specialization

What I Read: Visualizing Weights

By Andrew Fairless on May 3, 2021April 10, 2021

https://distill.pub/2020/circuits/visualizing-weights/ Visualizing WeightsChelsea VossNick CammarataGabriel GohMichael PetrovLudwig SchubertBen EganSwee Kiat LimChris OlahFeb. 4, 2021DOI 10.23915/distill.00024.007 “The problem of understanding a neural network is a little bit like reverse engineering aContinue readingWhat I Read: Visualizing Weights

Year: 2021