optimization – Page 4 – Andrew Fairless, Ph.D.

What I Read: Deep Learning From First Principles

By Andrew Fairless on May 4, 2022April 25, 2022

https://horace.io/brrr_intro.html Making Deep Learning Go Brrrr From First PrinciplesHorace He “So, you want to improve the performance of your deep learning model. How might you approach such a task?”

What I Read: Researchers Build AI That Builds AI

By Andrew Fairless on March 30, 2022February 3, 2022

https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/ Researchers Build AI That Builds AIAnil AnanthaswamyJanuary 25, 2022“By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.”

What I Read: Deep Learning Optimization Theory

By Andrew Fairless on December 15, 2021November 16, 2021

https://towardsdatascience.com/deep-learning-optimization-theory-introduction-148b3504b20f?gi=ff0bd10cc9fe Deep Learning Optimization Theory — IntroductionUnderstanding the theory of optimization in deep learning is crucial to enable progress. This post introduces the experimental and theoretical approaches to studying it.OmriContinue readingWhat I Read: Deep Learning Optimization Theory

What I Read: Limits Discovered in Quest for Optimal Solutions

By Andrew Fairless on December 1, 2021November 16, 2021

https://www.quantamagazine.org/surprising-limits-discovered-in-quest-for-optimal-solutions-20211101/ Surprising Limits Discovered in Quest for Optimal SolutionsMax G. LevyContributing WriterNovember 1, 2021 “Algorithms that zero in on solutions to optimization problems are the beating heart of machine reasoning.Continue readingWhat I Read: Limits Discovered in Quest for Optimal Solutions

What I Read: machine learning with differential privacy

By Andrew Fairless on November 29, 2021November 16, 2021

https://differentialprivacy.org/how-to-deploy-ml-with-dp/ How to deploy machine learning with differential privacy?Posted by Nicolas Papernot and Abhradeep Thakurta on October 25, 2021. “In many applications of machine learning, such as machine learning forContinue readingWhat I Read: machine learning with differential privacy

What I Read: Neuron Bursts Can Mimic Famous AI Learning Strategy

By Andrew Fairless on November 1, 2021October 24, 2021

https://www.quantamagazine.org/brain-bursts-can-mimic-famous-ai-learning-strategy-20211018/ Neuron Bursts Can Mimic Famous AI Learning StrategyAllison WhittenOctober 18, 2021“A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation,Continue readingWhat I Read: Neuron Bursts Can Mimic Famous AI Learning Strategy

What I Read: Machine learning is not nonparametric statistics

By Andrew Fairless on October 20, 2021October 16, 2021

https://www.argmin.net/2021/10/13/prediction/ Machine learning is not nonparametric statistics.Ben RechtOct 13, 2021 “…I’ve been told by respected statisticians that machine learning is nothing more than nonparametric statistics. The longer I work inContinue readingWhat I Read: Machine learning is not nonparametric statistics

What I Learn: Scaling TensorFlow

By Andrew Fairless on October 12, 2021September 24, 2021

https://arxiv.org/abs/2109.09541 Scaling TensorFlow to 300 million predictions per secondJan Hartman, Davorin Kopič “In this work, we describe the process of scaling machine learning models implemented in the TensorFlow machine learningContinue readingWhat I Learn: Scaling TensorFlow

What I Read: Learning Neural Network Subspaces

By Andrew Fairless on September 20, 2021September 4, 2021

https://arxiv.org/abs/2102.10472 Learning Neural Network SubspacesMitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari “These neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance ofContinue readingWhat I Read: Learning Neural Network Subspaces

What I Read: Computer Scientists Discover Limits of Major Research Algorithm

By Andrew Fairless on September 7, 2021August 17, 2021

https://www.quantamagazine.org/computer-scientists-discover-limits-of-major-research-algorithm-20210817/ Computer Scientists Discover Limits of Major Research AlgorithmNick ThiemeAugust 17, 2021 “Many aspects of modern applied research rely on a crucial algorithm called gradient descent…. researchers have never fullyContinue readingWhat I Read: Computer Scientists Discover Limits of Major Research Algorithm

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