semi-supervised – Andrew Fairless, Ph.D.

What I Read: Causal Inference

By Andrew Fairless on October 25, 2022September 9, 2022

https://thegradient.pub/causal-inference-connecting-data-and-reality/ Causal Inference: Connecting Data and RealityWenwen Ding04.Sep.2022 “Causal inference is a theory that describes, discriminates, and measures causal relationships, developed from statistics.”

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: SwAV method

By Andrew Fairless on August 2, 2021July 12, 2021

https://m0nads.wordpress.com/2021/01/08/the-swav-method/ The SwAV methodAntonio Ferraioli “The SwAV method is a clustering-based online method whose goal is to learn visual features in an online fashion without supervision.”

What I Read: Representation quality, complexity

By Andrew Fairless on July 29, 2021July 12, 2021

https://willwhitney.com/representation-quality-and-the-complexity-of-learning.html Will WhitneyRepresentation quality and the complexity of learning “In the last few years, there’s been an explosion of work on learning good representations of data…. However, defining precisely whatContinue readingWhat I Read: Representation quality, complexity

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: 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: Snorkel Tutorial to Predict Multiple Sclerosis

By Andrew Fairless on January 13, 2021December 23, 2020

https://medium.com/swlh/a-snorkel-tutorial-using-clinical-notes-to-predict-multiple-sclerosis-severity-scores-e3863801630f A Snorkel Tutorial Using Clinical Notes to Predict Multiple Sclerosis Severity ScoresNLP4HJul 13 “In this tutorial, we will walk through the process of using Snorkel to generate labels forContinue readingWhat I Read: Snorkel Tutorial to Predict Multiple Sclerosis

Tag: semi-supervised