active learning – Andrew Fairless, Ph.D.

What I Learn: neural scaling, data pruning

By Andrew Fairless on April 11, 2023February 22, 2023

https://arxiv.org/abs/2206.14486 Beyond neural scaling laws: beating power law scaling via data pruningBen Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos[Submitted on 29 Jun 2022 (v1), last revised 15Continue readingWhat I Learn: neural scaling, data pruning

What I Read: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

What I Read: Against Naive AI Scaling

By Andrew Fairless on July 27, 2022June 27, 2022

https://jacobbuckman.com/2022-06-14-an-actually-good-argument-against-naive-ai-scaling/ An Actually-Good Argument Against Naive AI ScalingJacob BuckmanPosted on June 14, 2022 “…the debate is whether scaled-up language models in the style of GPT-3 will eventually become general intelligences,Continue readingWhat I Read: Against Naive AI Scaling

What I Read: Learning, not Enough Data Part 3

By Andrew Fairless on June 1, 2022May 23, 2022

https://lilianweng.github.io/posts/2022-04-15-data-gen/ Learning with not Enough Data Part 3: Data GenerationApril 15, 2022Lilian Weng “Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2).Continue readingWhat I Read: Learning, not Enough Data Part 3

What I Read: Bootstrapping Labels

By Andrew Fairless on April 27, 2022March 12, 2022

https://thegradient.pub/bootstrapping-labels-via-_-supervision-human-in-the-loop/ Bootstrapping Labels via _ Supervision & Human-In-The-LoopEugene Yan05.Mar.2022 “Collecting training labels is a seldom discussed art…. In this write-up, we’ll discuss semi, active, and weakly supervised learning, and seeContinue readingWhat I Read: Bootstrapping Labels

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: Gaussian Process, Active Learning in Physics

By Andrew Fairless on January 17, 2022December 11, 2021

https://towardsdatascience.com/gaussian-process-first-step-towards-active-learning-in-physics-239a8b260579?gi=41cc7e15ddd0 Gaussian Process: First Step Towards Active Learning in PhysicsMaxim ZiatdinovNov 1 “An approach to address these problems is a Gaussian Process…. we would like to offer a slightly unusualContinue readingWhat I Read: Gaussian Process, Active Learning in Physics

What I Read: Identifying Document Types

By Andrew Fairless on August 11, 2021July 22, 2021

https://tech.scribd.com/blog/2021/identifying-document-types.html Identifying Document Types at ScribdJonathan RamkissoonJuly 12, 2021 “User-uploaded documents have been a core component of Scribd’s business from the very beginning, understanding what is actually in the documentContinue readingWhat I Read: Identifying Document Types

What I Read: Production with Deep Semi-Supervised Learning

By Andrew Fairless on January 28, 2021January 2, 2021

https://towardsdatascience.com/from-research-to-production-with-deep-semi-supervised-learning-7caaedc39093 From Research to Production with Deep Semi-Supervised LearningVarun NairSep 25 “Semi-supervised learning (SSL), a subfield that combines both supervised and unsupervised learning, has grown in popularity in the deepContinue readingWhat I Read: Production with Deep Semi-Supervised Learning

What I Read: Continual Learning… and the Bridge to Active and Open World Learning

By Andrew Fairless on January 22, 2021December 30, 2020

https://arxiv.org/abs/2009.01797 A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningMartin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan RameshContinue readingWhat I Read: Continual Learning… and the Bridge to Active and Open World Learning

Tag: active learning