neural network – Page 18 – Andrew Fairless, Ph.D.

What I Read: How to Train Really Large Models

By Andrew Fairless on October 26, 2021October 16, 2021

https://lilianweng.github.io/lil-log/2021/09/24/train-large-neural-networks.html How to Train Really Large Models on Many GPUs?Sep 24, 2021Lilian Weng “How to train large and deep neural networks is challenging, as it demands a large amount ofContinue readingWhat I Read: How to Train Really Large Models

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 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: 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: Understanding Convolutions on Graphs

By Andrew Fairless on September 29, 2021September 21, 2021

https://distill.pub/2021/understanding-gnns/ Understanding Convolutions on GraphsUnderstanding the building blocks and design choices of graph neural networks.Ameya DaigavaneBalaraman RavindranGaurav AggarwalGoogle ResearchSept. 2, 202110.23915/distill.00032 “Graph neural networks (GNNs) are a family of neuralContinue readingWhat I Read: Understanding Convolutions on Graphs

What I Read: Introduction to Graph Neural Networks

By Andrew Fairless on September 28, 2021September 21, 2021

https://distill.pub/2021/gnn-intro/ A Gentle Introduction to Graph Neural NetworksBenjamin Sanchez-LengelingEmily ReifAdam PearceAlex WiltschkoGoogle ResearchSept. 2, 202110.23915/distill.00033 “Neural networks have been adapted to leverage the structure and properties of graphs. We exploreContinue readingWhat I Read: Introduction to Graph Neural Networks

What I Read: How Computationally Complex Is a Neuron?

By Andrew Fairless on September 27, 2021September 21, 2021

https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/ How Computationally Complex Is a Single Neuron?by Allison Whitten “Computational neuroscientists taught an artificial neural network to imitate a biological neuron. The result offers a new way to thinkContinue readingWhat I Read: How Computationally Complex Is a Neuron?

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: Graph Theory Into New Dimensions

By Andrew Fairless on September 16, 2021September 4, 2021

https://www.quantamagazine.org/how-big-data-carried-graph-theory-into-new-dimensions-20210819/ How Big Data Carried Graph Theory Into New DimensionsStephen OrnesContributing WriterAugust 19, 2021 ““Now we know that the network is just the shadow of the thing,”… If a dataContinue readingWhat I Read: Graph Theory Into New Dimensions

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

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