attention – Page 4 – Andrew Fairless, Ph.D.

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: Advances in TF-Ranking

By Andrew Fairless on August 31, 2021August 17, 2021

https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-RankingTuesday, July 27, 2021 Posted by Michael Bendersky and Xuanhui Wang, Software Engineers, Google Research“LTR models — unlike standard classification models that classify one item at aContinue readingWhat I Read: Advances in TF-Ranking

What I Read: demystifying graph deep learning

By Andrew Fairless on August 30, 2021August 17, 2021

https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/ An attempt at demystifying graph deep learningEric Ma “There are a ton of great explainers of what graph neural networks are. However, I find that a lot of themContinue readingWhat I Read: demystifying graph deep learning

What I Read: Do Multi-Task Learning Intelligently

By Andrew Fairless on July 15, 2021July 2, 2021

https://thegradient.pub/how-to-do-multi-task-learning-intelligently/ How to Do Multi-Task Learning Intelligently22.Jun.2021 “Traditionally, a single machine learning model is devoted to one task… There are some advantages, however, to training models to make multiple kindsContinue readingWhat I Read: Do Multi-Task Learning Intelligently

What I Read: Reducing High Cost of Training NLP Models

By Andrew Fairless on March 29, 2021April 3, 2021

https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/ Reducing the High Cost of Training NLP Models With SRU++By Tao Lei, PhDResearch Leader and Scientist at ASAPP “The Transformer architecture was proposed to accelerate model training in NLP….Continue readingWhat I Read: Reducing High Cost of Training NLP Models

What I Read: How Transformers work

By Andrew Fairless on March 9, 2021March 7, 2021

https://theaisummer.com/transformer/ How Transformers work in deep learning and NLP: an intuitive introductionNatural Language ProcessingNikolas AdaloglouDec 24, 2020 “In my opinion, transformers are not so hard to grasp. It’s the combinationContinue readingWhat I Read: How Transformers work

What I Read: Data-efficient image Transformers

By Andrew Fairless on March 5, 2021March 5, 2021

Data-efficient image Transformers: A promising new technique for image classificationDecember 23, 2020 “We’ve developed a new method to train computer vision models that leverage Transformers…. Our new technique — Data-efficientContinue readingWhat I Read: Data-efficient image Transformers

What I Read: Introduction to Graph Neural Networks

By Andrew Fairless on February 23, 2021April 3, 2021

https://blog.exxactcorp.com/a-friendly-introduction-to-graph-neural-networks/ Deep LearningA Friendly Introduction to Graph Neural NetworksMarketing, November 11, 2020 “Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured asContinue readingWhat I Read: Introduction to Graph Neural Networks

What I Read: Attention with Performers

By Andrew Fairless on February 7, 2021January 11, 2021

https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Rethinking Attention with PerformersFriday, October 23, 2020Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research “To resolve these issues, we introduce the Performer, a Transformer architecture withContinue readingWhat I Read: Attention with Performers

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

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