machine learning – Page 2 – Andrew Fairless, Ph.D.

What I Read: passively learned, causality

By Andrew Fairless on December 4, 2024August 26, 2024

What can be passively learned about causality?Simons InstituteAndrew Lampinen (Google DeepMind)Jun 25, 2024 “What could language models learn about causality and experimentation from their passive training?”

What I Read: Evaluating LLM-Evaluators

By Andrew Fairless on December 3, 2024August 26, 2024

https://eugeneyan.com/writing/llm-evaluators Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)Eugene Yan “After reading this, you’ll gain an intuition on how to apply, evaluate, and operate LLM-evaluators. We’ll learn when to apply (i)Continue readingWhat I Read: Evaluating LLM-Evaluators

What I Read: sparsity, PyTorch, Hadamard product

By Andrew Fairless on December 2, 2024August 26, 2024

https://alexshtf.github.io/2024/07/07/HadamardParameterization.html Alex ShtoffFun with sparsity in PyTorch via Hadamard product parametrizationJul 7, 2024 “The beauty of sparsity inducing regularization is that we let our optimizer discover the sparsity patterns, insteadContinue readingWhat I Read: sparsity, PyTorch, Hadamard product

What I Read: tilted loss

By Andrew Fairless on November 25, 2024August 26, 2024

https://alexshtf.github.io/2024/06/14/Untilting.html Alex ShtoffUntilting the tilted lossJun 14, 2024 “Typically in machine learning we train a model by minimizing the average loss…. The parameter t can be thought as a kindContinue readingWhat I Read: tilted loss

What I Read: Classifying pdfs

By Andrew Fairless on November 21, 2024August 26, 2024

https://snats.xyz/pages/articles/classifying_a_bunch_of_pdfs.html Classifying all of the pdfs on the internetSantiago Pedroza2024-08-18 “How would you classify all the pdfs in the internet? Well, that is what I tried doing this time.”

What I Read: LLM Pre-training Post-training

By Andrew Fairless on November 18, 2024August 26, 2024

https://magazine.sebastianraschka.com/p/new-llm-pre-training-and-post-training New LLM Pre-training and Post-training ParadigmsA Look at How Modern LLMs Are TrainedSebastian Raschka, PhDAug 17, 2024 “Initially, the LLM training process focused solely on pre-training, but it hasContinue readingWhat I Read: LLM Pre-training Post-training

What I Read: Open-endedness, Agentic AI

By Andrew Fairless on November 13, 2024August 25, 2024

https://press.airstreet.com/p/open-endedness-is-all-well-need Open-endedness is all we’ll need: On “Agentic AI”Air Street Capital, Nathan Benaich, and Alex ChalmersAug 15, 2024 “Agentic systems offer the promise of agents – a software system thatContinue readingWhat I Read: Open-endedness, Agentic AI

What I Read: Regularization, polynomial bases

By Andrew Fairless on November 7, 2024August 25, 2024

https://alexshtf.github.io/2024/06/03/PolynomialBasesRegProps.html Alex ShtoffRegularization properties of polynomial basesJun 3, 2024 “We used the Bernstein basis to demonstrate the importance of chosing a “good” polynomial basis, and that other well-known bases mayContinue readingWhat I Read: Regularization, polynomial bases

What I Read: Contextual Bandit, LinUCB:

By Andrew Fairless on November 5, 2024August 15, 2024

https://truetheta.io/concepts/reinforcement-learning/lin-ucb A Reliable Contextual Bandit Algorithm: LinUCBDJ RichAugust 6, 2024 “A user visits a news website. Which articles should they be shown?”

What I Read: Visual Guide, Quantization

By Andrew Fairless on October 30, 2024August 3, 2024

https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization A Visual Guide to Quantization, Demystifying the Compression of Large Language ModelsMaarten GrootendorstJul 22, 2024 “…I will introduce the field of quantization in the context of language modeling andContinue readingWhat I Read: Visual Guide, Quantization

Tag: machine learning