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

What I Read: LLM Training, RLHF

By Andrew Fairless on October 30, 2023October 5, 2023

https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives LLM Training: RLHF and Its AlternativesSebastian Raschka, PhDSep 10, 2023 “RLHF is an integral part of the modern LLM training pipeline due to its ability to incorporate human preferencesContinue readingWhat I Read: LLM Training, RLHF

What I Read: Markov Chains

By Andrew Fairless on October 24, 2023September 9, 2023

https://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/ Markov Chains: Why Walk When You Can Flow?Richard McElreath28 November 2017 “If you are still using a Gibbs sampler, you are working too hard for too little result. Newer,Continue readingWhat I Read: Markov Chains

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: Improving a Machine Learning System (Part 1 – Broken Abstractions)

By Andrew Fairless on December 22, 2021November 16, 2021

https://danshiebler.com/2021-11-06-ml-systems-1/ Improving a Machine Learning System (Part 1 – Broken Abstractions)Dan ShieblerPosted on November 6, 2021 “Making measurable improvements to a mature machine learning system is extremely difficult. In thisContinue readingWhat I Read: Improving a Machine Learning System (Part 1 – Broken Abstractions)

What I Read: AutoML, Multi-task learning, Multi-tower models, Ads

By Andrew Fairless on April 20, 2021April 3, 2021

https://medium.com/pinterest-engineering/how-we-use-automl-multi-task-learning-and-multi-tower-models-for-pinterest-ads-db966c3dc99e How we use AutoML, Multi-task learning and Multi-tower models for Pinterest AdsPinterest EngineeringAug 19, 2020Ernest Wang | Software Engineer, Ads Ranking “In this blog post, we explain how keyContinue readingWhat I Read: AutoML, Multi-task learning, Multi-tower models, Ads

What I Read: Statistics, Geometry Problem

By Andrew Fairless on April 6, 2021March 22, 2021

https://www.quantamagazine.org/statistics-postdoc-tames-decades-old-geometry-problem-20210301/ Statistics Postdoc Tames Decades-Old Geometry ProblemTo the surprise of experts in the field, a postdoctoral statistician has solved one of the most important problems in high-dimensional convex geometry.Erica KlarreichMarchContinue readingWhat I Read: Statistics, Geometry Problem

What I Read: Why I’m lukewarm on graph neural networks

By Andrew Fairless on March 10, 2021March 7, 2021

https://www.singlelunch.com/2020/12/28/why-im-lukewarm-on-graph-neural-networks/ Why I’m lukewarm on graph neural networksMatt RangerFebruary 16, 2020 “I’m only lukewarm on Graph Neural Networks (GNNs)…. I think some progress can come of this research, but we’reContinue readingWhat I Read: Why I’m lukewarm on graph neural networks

Tag: sampling