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

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

What I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

By Andrew Fairless on October 5, 2021September 21, 2021

https://arxiv.org/abs/2109.02869v1 The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement LearningYujin Tang, David Ha “…we build systems that feed each sensory input from the environment into distinct, butContinue readingWhat I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

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: 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: Prompting, Language Models, NLP

By Andrew Fairless on August 9, 2021July 22, 2021

https://thegradient.pub/prompting/ Prompting: Better Ways of Using Language Models for NLP Tasks03.Jul.2021Tianyu Gao “The giant model size of GPT-3 is an important factor for its success, while the concept of promptsContinue readingWhat I Read: Prompting, Language Models, NLP

What I Read: Better computer vision models, Transformers, CNNs

By Andrew Fairless on July 27, 2021July 12, 2021

https://ai.facebook.com/blog/computer-vision-combining-transformers-and-convolutional-neural-networks/ Better computer vision models by combining Transformers and convolutional neural networksJuly 8, 2021 “We’ve developed a new computer vision model… which combines… convolutional neural networks (CNNs) and Transformer-based models…Continue readingWhat I Read: Better computer vision models, Transformers, CNNs

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