neural network – Page 17 – Andrew Fairless, Ph.D.

What I Read: Introduction AutoEncoder

By Andrew Fairless on November 15, 2021October 24, 2021

https://www.theaidream.com/post/an-introduction-to-autoencoder-and-variational-autoencoder-vae Introduction to AutoEncoder and Variational AutoEncoder(VAE)Nagesh Singh ChauhanJul 28“In recent years, deep learning-based generative models have gained more and more interest…. we will dive deep into these generative networksContinue readingWhat I Read: Introduction AutoEncoder

What I Read: How Train Large Deep Learning Models

By Andrew Fairless on November 8, 2021October 24, 2021

https://www.assemblyai.com/blog/how-to-train-large-deep-learning-models-as-a-startup/ How to Train Large Deep Learning Models as a StartupDylan FoxOct 7, 2021Training large deep learning models is expensive and slow. Yet, startups are all about iterating fast. InContinue readingWhat I Read: How Train Large Deep Learning Models

What I Read: The Uselessness of Useful Knowledge

By Andrew Fairless on November 2, 2021October 24, 2021

https://www.quantamagazine.org/science-has-entered-a-new-era-of-alchemy-good-20211020/ The Uselessness of Useful KnowledgeRobbert DijkgraafOctober 20, 2021Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress.“…the current state of AI research is nothing newContinue readingWhat I Read: The Uselessness of Useful Knowledge

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: 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

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