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

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 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: Deciding Which Tasks Should Train Together

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

https://ai.googleblog.com/2021/10/deciding-which-tasks-should-train.html Deciding Which Tasks Should Train Together in Multi-Task Neural NetworksMonday, October 25, 2021Posted by Christopher Fifty, Research Engineer, Google Research, Brain Team “…there may be instances when learning fromContinue readingWhat I Read: Deciding Which Tasks Should Train Together

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

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