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

What I Read: How diffusion models work

By Andrew Fairless on November 30, 2022October 20, 2022

https://theaisummer.com/diffusion-models/ How diffusion models work: the math from scratchSergios Karagiannakos, Nikolas Adaloglouon2022-09-29 “Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.”

What I Read: When use Bayesian optimization

By Andrew Fairless on October 10, 2022September 9, 2022

https://medium.com/@eduardogarrido90/when-do-i-need-to-use-bayesian-optimization-bo-series-i-50531ba6952d When do I need to use Bayesian optimization? (BO series I)Eduardo C. Garrido MerchánAug 22 “When do I need to use Bayesian optimization? And the answer is suprising, becauseContinue readingWhat I Read: When use Bayesian optimization

What I Read: introduction to NumPyro

By Andrew Fairless on September 19, 2022August 22, 2022

https://dfm.io/posts/intro-to-numpyro/ An astronomer’s introduction to NumPyroDan Foreman-MackeyJul 28 2022 “In this post… I’ll focus primarily on providing an introduction to NumPyro, which is a probabilistic programming library that provides anContinue readingWhat I Read: introduction to NumPyro

What I Read: Short Time Series

By Andrew Fairless on September 1, 2022August 3, 2022

https://minimizeregret.com/short-time-series-prior-knowledge Modeling Short Time Series with Prior KnowledgeWhat ‘Including Prior Information’ really looks like.Tim Radtke2019-04-16 “It is generally difficult to model time series when there is insuffient data to modelContinue readingWhat I Read: Short Time Series

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: Bandits for Recommender Systems

By Andrew Fairless on June 8, 2022May 23, 2022

https://eugeneyan.com/writing/bandits/ Bandits for Recommender Systemseugeneyan “Recommender systems work well when we have a lot of data on user-item preferences…. Conversely, with very little data, we have low certainty…. Bandits addressContinue readingWhat I Read: Bandits for Recommender Systems

What I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Andrew Fairless on June 7, 2022May 23, 2022

https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/ Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw DataCharlie WoodStaff WriterMay 10, 2022 “Researchers say we’re on the cusp of “GoPro physics,” where a camera can pointContinue readingWhat I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

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: MCMC for big datasets

By Andrew Fairless on February 22, 2022January 20, 2022

https://www.pymc-labs.io/blog-posts/pymc-stan-benchmark/ MCMC for big datasets: faster sampling with JAX and the GPUScaling PyMC using JAX to sample on the GPUAuthored by Martin Ingram on 2021-12-22 “You’ll often hear people sayContinue readingWhat I Read: MCMC for big datasets

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

Tag: Bayesian