transformer – Page 9 – Andrew Fairless, Ph.D.

What I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

By Andrew Fairless on February 6, 2021January 11, 2021

https://arxiv.org/abs/1912.02292 Deep Double Descent: Where Bigger Models and More Data HurtPreetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever “We show that a variety of modern deepContinue readingWhat I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

What I Read: Transformers for Image Recognition

By Andrew Fairless on January 31, 2021January 6, 2021

https://medium.com/swlh/an-image-is-worth-16×16-words-transformers-for-image-recognition-at-scale-brief-review-of-the-8770a636c6a8 An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale (Brief Review of the ICLR 2021 Paper)Stan KriventsovOct 9 “The reason attention models haven’t been doing betterContinue readingWhat I Read: Transformers for Image Recognition

What I Read: Transformer Architecture

By Andrew Fairless on January 21, 2021December 29, 2020

https://blog.exxactcorp.com/a-deep-dive-into-the-transformer-architecture-the-development-of-transformer-models/ Deep LearningA Deep Dive Into the Transformer Architecture – The Development of Transformer ModelsMarketing, July 14, 2020 0 11 min readTransformers for Natural Language Processing “There’s no better timeContinue readingWhat I Read: Transformer Architecture

What I Read: Progress of Natural Language Processing

By Andrew Fairless on January 5, 2021December 22, 2020

https://blog.exxactcorp.com/the-unreasonable-progress-of-deep-neural-networks-in-natural-language-processing-nlp/ Deep LearningThe Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)Marketing, June 2, 2020 0 14 min read “With the advent of pre-trained generalized language models, weContinue readingWhat I Read: Progress of Natural Language Processing

What I Read: GPT-3, The New Mighty Language Model

By Andrew Fairless on December 28, 2020December 20, 2020

https://towardsdatascience.com/gpt-3-the-new-mighty-language-model-from-openai-a74ff35346fc?gi=a6255cca8370 GPT-3: The New Mighty Language Model from OpenAIPushing Deep Learning to the Limit with 175B ParametersMoiz SaifeeMay 31 “While this type of transfer learning obviates the need to useContinue readingWhat I Read: GPT-3, The New Mighty Language Model

What I Read: Neural Networks to Find Answers in Tables

By Andrew Fairless on December 24, 2020December 13, 2020

https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html?ref=hvper.com Using Neural Networks to Find Answers in TablesThursday, April 30, 2020Posted by Thomas Müller, Software Engineer, Google Research “Much of the world’s information is stored in the form ofContinue readingWhat I Read: Neural Networks to Find Answers in Tables

What I Read: Common Sense Computers

By Andrew Fairless on December 23, 2020December 10, 2020

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/ artificial intelligenceCommon Sense Comes Closer to ComputersThe problem of common-sense reasoning has plagued the field of artificial intelligence for over 50 years. Now a new approach, borrowing from twoContinue readingWhat I Read: Common Sense Computers

What I Read: Transformers Graph Neural Networks

By Andrew Fairless on December 17, 2020December 4, 2020

https://graphdeeplearning.github.io/post/transformers-are-gnns/ Transformers are Graph Neural NetworksChaitanya JoshiFeb 12, 2020 “Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behindContinue readingWhat I Read: Transformers Graph Neural Networks

What I Read: Reformer efficient Transformer

By Andrew Fairless on December 10, 2020December 13, 2020

https://towardsdatascience.com/illustrating-the-reformer-393575ac6ba0?gi=34b920510f6f Illustrating the ReformerThe efficient TransformerAlireza DirafzoonFeb 4 “Recently, Google introduced the Reformer architecture, a Transformer model designed to efficiently handle processing very long sequences of data (e.g. up toContinue readingWhat I Read: Reformer efficient Transformer

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