linear algebra – Page 4 – Andrew Fairless, Ph.D.

What I Read: Open Source, AlphaTensor

By Andrew Fairless on June 13, 2023May 1, 2023

https://www.kdnuggets.com/2023/03/first-open-source-implementation-deepmind-alphatensor.html First Open Source Implementation of DeepMind’s AlphaTensorDiego Fiori, Co-founder & CTO at NebulyMarch 10, 2023 “The first open-source implementation of AlphaTensor has been released and opens the door forContinue readingWhat I Read: Open Source, AlphaTensor

What I Read: Matrix Multiplication

By Andrew Fairless on January 30, 2023December 4, 2022

https://www.quantamagazine.org/ai-reveals-new-possibilities-in-matrix-multiplication-20221123/ AI Reveals New Possibilities in Matrix MultiplicationBen BrubakerNovember 23, 2022 “Inspired by the results of a game-playing neural network, mathematicians have been making unexpected advances on an age-old mathContinue readingWhat I Read: Matrix Multiplication

What I Read: Russian Roulette

By Andrew Fairless on January 3, 2023November 5, 2022

The Russian Roulette: An Unbiased Estimator of the LimitBy Fabian Pedregosa.Sat 15 October 2022 “The Russian Roulette offers a simple way to construct an unbiased estimator for the limit ofContinue readingWhat I Read: Russian Roulette

What I Read: Sins, Numerical Linear Algebra

By Andrew Fairless on December 7, 2022October 20, 2022

https://nhigham.com/2022/10/11/seven-sins-of-numerical-linear-algebra/ Seven Sins of Numerical Linear AlgebraNick HighamPosted on October 11, 2022 by Nick Higham “In numerical linear algebra we are concerned with solving linear algebra problems accurately and efficientlyContinue readingWhat I Read: Sins, Numerical Linear Algebra

What I Read: Neural Tangent Kernel

By Andrew Fairless on November 2, 2022September 27, 2022

https://lilianweng.github.io/posts/2022-09-08-ntk/ Some Math behind Neural Tangent KernelLilian WengSeptember 8, 2022 “Neural tangent kernel… leads to great insights into why neural networks with enough width can consistently converge to a globalContinue readingWhat I Read: Neural Tangent Kernel

What I Read: AI Researcher, Bitter Medicine

By Andrew Fairless on October 20, 2022September 9, 2022

https://www.quantamagazine.org/the-ai-researcher-giving-her-field-its-bitter-medicine-20220830/ The AI Researcher Giving Her Field Its Bitter MedicineMax G. LevyAugust 30, 2022 “In practice, matrix methods in machine learning can’t effectively capture higher-order relationships. Essentially, you can’t learnContinue readingWhat I Read: AI Researcher, Bitter Medicine

What I Read: Emergent Features

By Andrew Fairless on October 6, 2022August 22, 2022

LLM.int8() and Emergent Features LLM.int8() and Emergent Features2022-08-17 by Tim Dettmers “…I use advanced quantization methods to achieve no performance degradation transformer inference at scale that makes large models moreContinue readingWhat I Read: Emergent Features

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: 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: Einstein Summation in Deep Learning

By Andrew Fairless on January 26, 2022December 11, 2021

https://rockt.github.io/2018/04/30/einsum Einsum is All you Need – Einstein Summation in Deep LearningTim Rocktäschel30/04/2018 – updated 02/05/2018 “If you are anything like me, you find it difficult to remember the namesContinue readingWhat I Read: Einstein Summation in Deep Learning

Tag: linear algebra