transfer learning – Andrew Fairless, Ph.D.

What I Read: Data Selection, LLMs

By Andrew Fairless on June 12, 2024April 15, 2024

https://www.cs.princeton.edu/~smalladi/blog/2024/04/04/dataselection Using LESS Data to Tune Models: Data Selection in the Era of LLMsMengzhou Xia and Sadhika MalladiApril 04 2024 “We describe how data selection for modern-day LLMs differs fromContinue readingWhat I Read: Data Selection, LLMs

What I Read: Multimodal, Embeddings

By Andrew Fairless on October 5, 2023September 4, 2023

https://app.twelvelabs.io/blog/multimodal-embeddings The Multimodal Evolution of Vector EmbeddingsJames LeAugust 9, 2023 “…companies collect and feed hundreds of millions of terabytes of multimodal data into deep learning models. As a result, embeddings…Continue readingWhat I Read: Multimodal, Embeddings

What I Read: Transformers Training

By Andrew Fairless on January 19, 2023December 16, 2022

https://www.borealisai.com/research-blogs/tutorial-17-transformers-iii-training/ Tutorial #17: Transformers III Training08/06/2021P. Xu, S. Prince “…we discuss challenges with transformer training dynamics and introduce some of the tricks that practitioners use to get transformers to converge.”

What I Read: Pre-Trained Models, Robotics

By Andrew Fairless on December 21, 2022November 5, 2022

https://sergeylevine.substack.com/p/general-purpose-pre-trained-models General-Purpose Pre-Trained Models in RoboticsCan we (pre-) train policies to control any robot for any task?Sergey LevineOct 16 “In robotics, learning policies from a small amount of data isContinue readingWhat I Read: Pre-Trained Models, Robotics

What I Read: Zero-Shot, K-Shot Learning

By Andrew Fairless on October 18, 2022September 9, 2022

https://www.datature.io/blog/zero-shot-and-k-shot-learning Comprehensive Guide to Zero-Shot and K-Shot LearningLeonard SoAugust 23, 2022 “Few-shot learning is a type of supervised learning that is intended to rapidly generalise to new tasks containing onlyContinue readingWhat I Read: Zero-Shot, K-Shot Learning

What I Read: Supervised Contrastive Learning

By Andrew Fairless on May 30, 2022April 25, 2022

https://hazyresearch.stanford.edu/blog/2022-04-19-contrastive-2 Improving Transfer and Robustness in Supervised Contrastive LearningApr 19, 2022 Mayee Chen, Dan Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian , and Chris Ré. “A major problemContinue readingWhat I Read: Supervised Contrastive Learning

What I Read: Semi-Supervised Learning

By Andrew Fairless on January 31, 2022December 12, 2021

https://lilianweng.github.io/lil-log/2021/12/05/semi-supervised-learning.html Learning with not Enough Data Part 1: Semi-Supervised LearningDec 5, 2021 by Lilian Weng “When facing a limited amount of labeled data for supervised learning tasks, four approaches areContinue readingWhat I Read: Semi-Supervised Learning

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: Reducing Toxicity in Language Models

By Andrew Fairless on May 10, 2021April 17, 2021

https://lilianweng.github.io/lil-log/2021/03/21/reducing-toxicity-in-language-models.html Reducing Toxicity in Language Modelsby Lilian WengMar 21, 2021 “Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior andContinue readingWhat I Read: Reducing Toxicity in Language 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

Tag: transfer learning