sampling – Andrew Fairless, Ph.D.

What I Read: standard error

By Andrew Fairless on October 22, 2024July 22, 2024

http://www.stat.columbia.edu/~gelman/research/published/standarderror.pdf What is a standard error?Andrew Gelman7 September 2023 “In statistics, the standard error has a clear technical definition… In practice, though, challenges arise when we go beyond the simpleContinue readingWhat I Read: standard error

What I Read: Labeling, Uncertainty Sampling

By Andrew Fairless on July 30, 2024May 24, 2024

https://code.groundlight.ai/python-sdk/blog/active-learning Reducing Data Labeling Costs with Uncertainty SamplingTed SandlerMay 15, 2024Senior Applied Scientist “With active learning, only informative images are prioritized for review, making it possible to label small aContinue readingWhat I Read: Labeling, Uncertainty Sampling

What I Read: Flow Matching

By Andrew Fairless on June 26, 2024April 23, 2024

https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html An introduction to Flow MatchingTor Fjelde, Emile Mathieu, Vincent Dutordoir “Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilisticContinue readingWhat I Read: Flow Matching

What I Read: diffusion distillation

By Andrew Fairless on May 13, 2024March 11, 2024

https://sander.ai/2024/02/28/paradox.html The paradox of diffusion distillationSander DielemanFebruary 28, 2024 “…let’s take a closer look at the various ways in which the number of sampling steps required to get good resultsContinue readingWhat I Read: diffusion distillation

What I Read: Diffusion models, new theoretical perspective

By Andrew Fairless on April 8, 2024March 21, 2024

https://www.chenyang.co/diffusion.html Diffusion models from scratch, from a new theoretical perspectiveChenyang Yuan2023 “This tutorial aims to introduce diffusion models from an optimization perspective…”

What I Read: Sampling Text Generation

By Andrew Fairless on March 19, 2024February 5, 2024

https://huyenchip.com//2024/01/16/sampling.html Sampling for Text GenerationChip Huyen1/15/24 “ML models are probabilistic…. This probabilistic nature makes AI great for creative tasks…. However, this probabilistic nature also causes inconsistency and hallucinations. It’s fatalContinue readingWhat I Read: Sampling Text Generation

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

Tag: sampling