generalization – Page 3 – Andrew Fairless, Ph.D.

What I Read: Neural-Control Family

By Andrew Fairless on January 10, 2022December 11, 2021

https://www.gshi.me/blog/NeuralControl/ Neural-Control Family: What Deep Learning + Control Enables in the Real WorldGuanya Shi “…is machine learning (especially deep learning) really ready to be deployed in safety-critical systems?”

What I Read: Autonomous Building of Composable Models

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

https://thegradient.pub/strong-ai-requires-autonomous-building-of-composable-models/ Strong AI Requires Autonomous Building of Composable Models30.Oct.2021Jonathan MuganDr. Jonathan Mugan is a principal scientist at DeUmbra and is the author of The Curiosity Cycle. “…AI must be ableContinue readingWhat I Read: Autonomous Building of Composable Models

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: Machine learning is just statistics + quantifier reversal

By Andrew Fairless on November 18, 2021November 16, 2021

https://jeremybernste.in/writing/ml-is-just-statistics Machine learning is just statistics + quantifier reversaljeremybernsteLos Angeles, 21 Oct 2021 “But in machine learning there’s a switcheroo—we select the sample of data first, and then we useContinue readingWhat I Read: Machine learning is just statistics + quantifier reversal

What I Read: First-Principles Theory of Neural Network Generalization

By Andrew Fairless on November 17, 2021November 16, 2021

https://natluk.net/a-first-principles-theory-of-neuralnetwork-generalization-the-berkeley-artificial-intelligence-research-blog/ A First-Principles Theory of Neural Network Generalization – The Berkeley Artificial Intelligence Research BlogNatLuk Community25 October 2021 “Perhaps the greatest of these mysteries has been the question of generalization:Continue readingWhat I Read: First-Principles Theory of Neural Network Generalization

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 Read: Mystery of Deep Learning

By Andrew Fairless on October 13, 2021October 12, 2021

https://www.quantamagazine.org/a-new-link-to-an-old-model-could-crack-the-mystery-of-deep-learning-20211011/ A New Link to an Old Model Could Crack the Mystery of Deep LearningTo help them explain the shocking success of deep neural networks, researchers are turning to olderContinue readingWhat I Read: Mystery of Deep Learning

What I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

By Andrew Fairless on October 5, 2021September 21, 2021

https://arxiv.org/abs/2109.02869v1 The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement LearningYujin Tang, David Ha “…we build systems that feed each sensory input from the environment into distinct, butContinue readingWhat I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

What I Read: Geometric, Deep Learning

By Andrew Fairless on August 16, 2021July 22, 2021

https://towardsdatascience.com/geometric-foundations-of-deep-learning-94cdd45b451d?gi=d7fa7307aa63 Geometric foundations of Deep LearningGeometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principlesContinue readingWhat I Read: Geometric, Deep Learning

What I Read: Training AI, Analogies

By Andrew Fairless on August 13, 2021July 22, 2021

https://www.quantamagazine.org/melanie-mitchell-trains-ai-to-think-with-analogies-20210714/ The Computer Scientist Training AI to Think With AnalogiesMelanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can makeContinue readingWhat I Read: Training AI, Analogies

Tag: generalization