recurrent – Page 2 – Andrew Fairless, Ph.D.

What I Read: Chaos Researchers Can Now Predict

By Andrew Fairless on November 10, 2022September 27, 2022

https://www.quantamagazine.org/ai-algorithm-foresees-chaotic-tipping-points-20220915/ Chaos Researchers Can Now Predict Perilous Points of No ReturnBen BrubakerSeptember 15, 2022 “A custom-built machine learning algorithm can predict when a complex system is about to switch toContinue readingWhat I Read: Chaos Researchers Can Now Predict

What I Read: Will Transformers Take Over Artificial Intelligence?

By Andrew Fairless on April 26, 2022March 12, 2022

https://www.quantamagazine.org/will-transformers-take-over-artificial-intelligence-20220310/ Will Transformers Take Over Artificial Intelligence?A simple algorithm that revolutionized how neural networks approach language is now taking on vision as well. It may not stop there.Stephen OrnesContributing WriterMarchContinue readingWhat I Read: Will Transformers Take Over Artificial Intelligence?

What I Read: Multi-task Prediction of Organ Dysfunction

By Andrew Fairless on August 25, 2021August 17, 2021

https://ai.googleblog.com/2021/07/multi-task-prediction-of-organ.html Multi-task Prediction of Organ Dysfunction in ICUsThursday, July 22, 2021 “Applying machine learning (ML) to electronic health records (EHRs) has shown promise in predicting clinical outcomes. However, many ofContinue readingWhat I Read: Multi-task Prediction of Organ Dysfunction

What I Read: Not Optimized By Jax, PyTorch, or Tensorflow

By Andrew Fairless on August 24, 2021August 17, 2021

Useful Algorithms That Are Not Optimized By Jax, PyTorch, or Tensorflow Useful Algorithms That Are Not Optimized By Jax, PyTorch, or TensorflowJuly 21 2021Christopher Rackauckas “It turns out that forContinue readingWhat I Read: Not Optimized By Jax, PyTorch, or Tensorflow

What I Read: Geometric, Deep Learning

By Andrew Fairless on August 16, 2021July 22, 2021

https://towardsdatascience.com/geometric-foundations-of-deep-learning-94cdd45b451d?gi=d7fa7307aa63 Geometric foundations of Deep LearningGeometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principlesContinue readingWhat I Read: Geometric, Deep Learning

What I Read: Brain Implant Translates Thoughts of Writing Into Text

By Andrew Fairless on June 8, 2021May 30, 2021

https://www.wired.com/story/new-brain-implant-translates-thoughts-of-writing-into-text/ A New Brain Implant Translates Thoughts of Writing Into TextJohn Timmer, Ars TechnicaScience05.14.2021 09:00 AM “In early experiments, a paralyzed man with implants in his premotor cortex typed 90Continue readingWhat I Read: Brain Implant Translates Thoughts of Writing Into Text

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: Neural Text Generation

By Andrew Fairless on March 11, 2021March 7, 2021

https://lilianweng.github.io/lil-log/2021/01/02/controllable-neural-text-generation.html Controllable Neural Text GenerationJan 2, 2021Lilian Weng “How to steer a powerful unconditioned language model? In this post, we will delve into several approaches for controlled content generation withContinue readingWhat I Read: Neural Text Generation

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: 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

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