BERT – 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 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: 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: 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: Contrastive Representation Learning

By Andrew Fairless on July 1, 2021June 7, 2021

https://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html Contrastive Representation Learningby Lilian WengMay 31, 2021 “The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to eachContinue readingWhat I Read: Contrastive Representation Learning

What I Read: Transformer Networks to Answer Questions About Images

By Andrew Fairless on March 13, 2021March 7, 2021

https://medium.com/dataseries/microsoft-uses-transformer-networks-to-answer-questions-about-images-with-minimum-training-f978c018bb72 Microsoft Uses Transformer Networks to Answer Questions About Images With Minimum TrainingUnified VLP can understand concepts about scenic images by using pretrained models.Jesus RodriguezJan 12 “Can we build deepContinue readingWhat I Read: Transformer Networks to Answer Questions About Images

What I Read: Self Supervised Representation Learning in NLP

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

https://amitness.com/2020/05/self-supervised-learning-nlp/ Self Supervised Representation Learning in NLPPublished May 23, 2020 in nlp “While Computer Vision is making amazing progress on self-supervised learning only in the last few years, self-supervised learningContinue readingWhat I Read: Self Supervised Representation Learning in NLP

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

Tag: BERT