kernel – Andrew Fairless, Ph.D.

What I Read: Smooth Noisy Data

By Andrew Fairless on March 13, 2024January 25, 2024

https://towardsdatascience.com/the-perfect-way-to-smooth-your-noisy-data-4f3fe6b44440?gi=a6f62aaf2818 The Perfect Way to Smooth Your Noisy DataAndrew BowellOct 25, 2023 “Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.”

What I Read: Density Kernel Depth for Outlier Detection

By Andrew Fairless on January 24, 2024November 20, 2023

https://www.kdnuggets.com/density-kernel-depth-for-outlier-detection-in-functional-data Density Kernel Depth for Outlier Detection in Functional DataKulbir SinghNovember 8, 2023 “The Density Kernel Depth (DKD) method provides a nuanced approach to detect outliers in functional data…”

What I Read: Tree-Structured Parzen Estimator

By Andrew Fairless on June 19, 2023May 1, 2023

https://towardsdatascience.com/building-a-tree-structured-parzen-estimator-from-scratch-kind-of-20ed31770478 Building a Tree-Structured Parzen Estimator from Scratch (Kind Of)An alternative to traditional hyperparameter tuning methodsColin HorganApr 4 “Although popular, Grid and Random Search methods… are purely trial and error.Continue readingWhat I Read: Tree-Structured Parzen Estimator

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: Dataset Distillation

By Andrew Fairless on February 9, 2022January 20, 2022

https://ai.googleblog.com/2021/12/training-machine-learning-models-more.html Training Machine Learning Models More Efficiently with Dataset DistillationWednesday, December 15, 2021Posted by Timothy Nguyen1, Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research“For a machine learning (ML)Continue readingWhat I Read: Dataset Distillation

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: 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: Attention with Performers

By Andrew Fairless on February 7, 2021January 11, 2021

https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Rethinking Attention with PerformersFriday, October 23, 2020Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research “To resolve these issues, we introduce the Performer, a Transformer architecture withContinue readingWhat I Read: Attention with Performers

What I Read: Diffusion Map for manifold learning

By Andrew Fairless on December 19, 2020December 8, 2020

https://randomwalk.in/python/ml/2020/03/14/Diffusion-Map.html Diffusion Map for manifold learning, Theory and Implementation “Manifold learning is an approach to non-linear dimensionality reduction…. In this blog, we learn one of the many techniques in manifoldContinue readingWhat I Read: Diffusion Map for manifold learning

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