transformer – Page 6 – Andrew Fairless, Ph.D.

What I Read: Applying BERT to Speech

By Andrew Fairless on June 27, 2022June 27, 2022

https://thegradient.pub/an-illustrated-tour-of-applying-bert-to-speech-data/ An Illustrated Tour of Applying BERT to Speech DataJonathan Boigne10.May.2022 “Could we replace the text input in BERT with a speech sequence, mask a part of it, and similarlyContinue readingWhat I Read: Applying BERT to Speech

What I Read: Understanding, Simple AI

By Andrew Fairless on May 24, 2022April 25, 2022

https://www.quantamagazine.org/researchers-glimpse-how-ai-gets-so-good-at-language-processing-20220414/ Researchers Gain New Understanding From Simple AIMordechai RorvigStaff WriterApril 14, 2022 “Language processing programs are notoriously hard to interpret, but smaller versions can provide important insights into how theyContinue readingWhat I Read: Understanding, Simple AI

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: Textless NLP

By Andrew Fairless on April 13, 2022March 12, 2022

https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio Textless NLP: Generating expressive speech from raw audioSeptember 9, 2021 “GSLM leverages recent breakthroughs in representation learning, allowing it to work directly from only raw audio signals… for potentiallyContinue readingWhat I Read: Textless NLP

What I Read: To Understand Language is to Understand Generalization

By Andrew Fairless on February 16, 2022January 20, 2022

https://evjang.com/2021/12/17/lang-generalization.html To Understand Language is to Understand GeneralizationEric JangDec 17, 2021 “We all want ML models to generalize better, but defining “generalization” is hard. I suggest that the structure ofContinue readingWhat I Read: To Understand Language is to Understand Generalization

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 Learn: Meta-Learning, Keyphrase Extraction

By Andrew Fairless on January 18, 2022December 11, 2021

https://sinequa.medium.com/meta-learning-for-keyphrase-extraction-54ec960cd2ba Meta-Learning for Keyphrase ExtractionSinequaSep 6 “KeyPhrase Extraction (KPE) is the process of extracting relevant chunks of words from a document to best capture and represent its content…. we outlineContinue readingWhat I Learn: Meta-Learning, Keyphrase Extraction

What I Read: Dense Vectors

By Andrew Fairless on December 14, 2021November 16, 2021

https://www.pinecone.io/learn/dense-vector-embeddings-nlp/ Dense Vectors: Capturing Meaning with CodeJames BriggsData Scientist “But since the days of word2vec, developments in representing language have advanced at ludicrous speeds. This article will explore why weContinue readingWhat I Read: Dense Vectors

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: 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

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