regularization – Page 2 – Andrew Fairless, Ph.D.

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: Policy Regulariser, Adversary

By Andrew Fairless on May 11, 2022April 25, 2022

https://deepmindsafetyresearch.medium.com/your-policy-regulariser-is-secretly-an-adversary-14684c743d45 Your Policy Regulariser is Secretly an AdversaryDeepMind Safety ResearchMar 24 By Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega“Policy regularisation can beContinue readingWhat I Read: Policy Regulariser, Adversary

What I Read: Why Deep Learning Works

By Andrew Fairless on August 23, 2021August 17, 2021

https://moultano.wordpress.com/2020/10/18/why-deep-learning-works-even-though-it-shouldnt/ Why Deep Learning Works Even Though It Shouldn’tRyan Moulton’s ArticlesRyan Moulton “Stop talking about minima…. Nobody ever trains their model remotely close to convergence…. What really needs further researchContinue readingWhat I Read: Why Deep Learning Works

What I Read: Double Machine Learning for causal inference

By Andrew Fairless on July 21, 2021July 2, 2021

https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Double Machine Learning for causal inferenceHow Double Machine Learning for causal inference works, from the theoretical foundations to an example of application.Borja VelascoJun 25 “This post tries to explain,Continue readingWhat I Read: Double Machine Learning for causal inference

What I Read: Reproducing Deep Double Descent

By Andrew Fairless on February 7, 2021January 11, 2021

https://hippocampus-garden.com/double_descent/ Reproducing Deep Double DescentShion HondaJune 13, 2020 “…you’ve probably heard of bias-variance trade-off… But, do you know that there is a continuation of the U-shaped test risk curve? Surprisingly,Continue readingWhat I Read: Reproducing Deep Double Descent

What I Read: Bayesian Product Ranking

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

https://tech.wayfair.com/data-science/2020/01/bayesian-product-ranking-at-wayfair/ Bayesian Product Ranking at WayfairJanuary 20, 2020 “If we are not able to personalize a customer’s experience, for example, because they are a first-time customer and we do notContinue readingWhat I Read: Bayesian Product Ranking

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

What I Read: regularization of linear models

By Andrew Fairless on December 24, 2020December 13, 2020

https://explained.ai/regularization/index.html A visual explanation for regularization of linear modelsTerence Parr “What’s lacking is a simple and intuitive explanation for what exactly is going on during regularization. The goal of thisContinue readingWhat I Read: regularization of linear models

Tag: regularization