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

What I Read: Modern AI Art

By Andrew Fairless on March 6, 2023January 9, 2023

https://www.quantamagazine.org/the-physics-principle-that-inspired-modern-ai-art-20230105/ The Physics Principle That Inspired Modern AI ArtAnil AnanthaswamyJanuary 5, 2023 “Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink toContinue readingWhat I Read: Modern AI Art

What I Read: Convolutions, Probability

By Andrew Fairless on February 13, 2023December 16, 2022

https://www.countbayesie.com/blog/2022/11/30/understanding-convolutions-in-probability-a-mad-science-perspective Understanding Convolutions in Probability: A Mad-Science PerspectiveWill Kurt, Count BayesieDecember 01, 2022 “If you’ve ever done much mad science you’ll know that a major problem is selectively determining theContinue readingWhat I Read: Convolutions, Probability

What I Read: Estimating Model Performance

By Andrew Fairless on August 23, 2022July 18, 2022

https://pub.towardsai.net/estimating-model-performance-without-ground-truth-453b850dad9a Estimating Model Performance without Ground TruthIt’s possible, as long as you keep your probabilities calibratedMichał OleszakMay 12 “A model in production is like a baby: it needs to beContinue readingWhat I Read: Estimating Model Performance

What I Read: Weak Supervision

By Andrew Fairless on July 11, 2022June 27, 2022

https://www.kdnuggets.com/2022/05/weak-supervision-modeling-explained.html Weak Supervision Modeling, ExplainedBy Frederic Sala, Computer Sciences Department at the University of Winsconsin-MadisonMay 27, 2022 “Weak supervision is a way to obtain labels for training data points forContinue readingWhat I Read: Weak Supervision

What I Read: Generative Modeling by Estimating Gradients

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

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

What I Read: Bayesian Geometry

By Andrew Fairless on February 21, 2022January 20, 2022

https://towardsdatascience.com/on-bayesian-geometry-7755abf9f4d2 On Bayesian GeometryGeometric interpretation of probability distributionsMichael Larionov, PhD “Bayesian inference is based on the fact that we often don’t know the underlying distribution of data, so we needContinue readingWhat I Read: Bayesian Geometry

What I Read: How to assign partial credit on an exam of true-false questions

By Andrew Fairless on November 30, 2021November 16, 2021

https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/ How to assign partial credit on an exam of true-false questions?By Terence Tao1 June, 2016Updates on my research and expository papers, discussion of open problems, and other maths-related topics.Continue readingWhat I Read: How to assign partial credit on an exam of true-false questions

What I Read: binary cross-entropy, log loss

By Andrew Fairless on October 25, 2021October 16, 2021

https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a?gi=375ce73be21b Understanding binary cross-entropy / log loss: a visual explanationDaniel GodoyNov 21, 2018 “If you are training a binary classifier, chances are you are using binary cross-entropy / log lossContinue readingWhat I Read: binary cross-entropy, log loss

What I Read: Machine learning is not nonparametric statistics

By Andrew Fairless on October 20, 2021October 16, 2021

https://www.argmin.net/2021/10/13/prediction/ Machine learning is not nonparametric statistics.Ben RechtOct 13, 2021 “…I’ve been told by respected statisticians that machine learning is nothing more than nonparametric statistics. The longer I work inContinue readingWhat I Read: Machine learning is not nonparametric statistics

What I Read: Causal Reasoning in Probability Trees

By Andrew Fairless on February 11, 2021January 13, 2021

https://arxiv.org/abs/2010.12237 Algorithms for Causal Reasoning in Probability TreesTim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega “Probability trees are one of the simplest modelsContinue readingWhat I Read: Causal Reasoning in Probability Trees

Tag: probability