transformer – Page 7 – Andrew Fairless, Ph.D.

What I Read: Semantic Search

By Andrew Fairless on July 20, 2021July 2, 2021

https://www.pinecone.io/learn/semantic-search/ Semantic Search: Measuring Meaning From Jaccard to BertJames BriggsData Scientist “Similarity search is a complex topic and there are countless techniques for building effective search engines. In this article,Continue readingWhat I Read: Semantic Search

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: 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: Language Model Fine-tuning

By Andrew Fairless on March 26, 2021March 7, 2021

https://ruder.io/recent-advances-lm-fine-tuning/ Recent Advances in Language Model Fine-tuningThis article provides an overview of recent methods to fine-tune large pre-trained language models.Sebastian Ruder24 Feb 2021 “While pre-training is compute-intensive, fine-tuning can beContinue readingWhat I Read: Language Model Fine-tuning

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: 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: Why I’m lukewarm on graph neural networks

By Andrew Fairless on March 10, 2021March 7, 2021

https://www.singlelunch.com/2020/12/28/why-im-lukewarm-on-graph-neural-networks/ Why I’m lukewarm on graph neural networksMatt RangerFebruary 16, 2020 “I’m only lukewarm on Graph Neural Networks (GNNs)…. I think some progress can come of this research, but we’reContinue readingWhat I Read: Why I’m lukewarm on graph neural networks

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

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