neural network – Page 15 – Andrew Fairless, Ph.D.

What I Read: Engineering Trade-Offs in Automatic Differentiation

By Andrew Fairless on March 8, 2022January 20, 2022

https://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/ Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and JuliaDecember 25 2021Author: Christopher Rackauckas “To understand the differences between automatic differentiation libraries, let’s talk about theContinue readingWhat I Read: Engineering Trade-Offs in Automatic Differentiation

What I Read: Generative Modeling by Estimating Gradients

By Andrew Fairless on March 7, 2022January 20, 2022

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

What I Read: AI Accelerators, Very Rich Landscape

By Andrew Fairless on March 2, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-iv-the-very-rich-landscape-17481be80917 AI Accelerators — Part IV: The Very Rich LandscapeAdi FuchsDec 5 “In this article, we will go over the current state of the AI hardware industry and present anContinue readingWhat I Read: AI Accelerators, Very Rich Landscape

What I Read: AI Accelerators, Architectural Foundations

By Andrew Fairless on March 1, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-iii-architectural-foundations-3f1f73d61f1f AI Accelerators — Part III: Architectural FoundationsAdi FuchsDec 5 “Many different companies build AI accelerators. The landscape is filled with an abundance of ideas and assumptions based on theContinue readingWhat I Read: AI Accelerators, Architectural Foundations

What I Read: AI Accelerators

By Andrew Fairless on February 28, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-ii-transistors-and-pizza-or-why-do-we-need-accelerators-75738642fdaa AI Accelerators — Part II: Transistors and Pizza (or: Why Do We Need Accelerators)?Adi FuchsDec 5 “There can not be a “one size fits all” approach when it comesContinue readingWhat I Read: AI Accelerators

What I Read: Interpretable Time Series

By Andrew Fairless on February 14, 2022January 20, 2022

https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html Interpretable Deep Learning for Time Series ForecastingMonday, December 13, 2021Posted by Sercan O. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud “Multi-horizon forecasting, i.e. predicting variables-of-interest atContinue readingWhat I Read: Interpretable Time Series

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: AI Researchers Fight Noise by Turning to Biology

By Andrew Fairless on February 1, 2022December 12, 2021

https://www.quantamagazine.org/ai-researchers-fight-noise-by-turning-to-biology-20211207/ AI Researchers Fight Noise by Turning to BiologyAllison WhittenContributing WriterDecember 7, 2021 “Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking toContinue readingWhat I Read: AI Researchers Fight Noise by Turning to Biology

What I Read: Semi-Supervised Learning

By Andrew Fairless on January 31, 2022December 12, 2021

https://lilianweng.github.io/lil-log/2021/12/05/semi-supervised-learning.html Learning with not Enough Data Part 1: Semi-Supervised LearningDec 5, 2021 by Lilian Weng “When facing a limited amount of labeled data for supervised learning tasks, four approaches areContinue readingWhat I Read: Semi-Supervised Learning

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: neural network