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In this post, we provide a Bayesian inference framework for in-context learning in large language models like GPT-3 and show empirical evidence for our framework, highlighting the differences from traditional supervised learning. This blog post primarily draws from the theoretical framework for in-context learning from An Explanation of In-context Learning as Implicit Bayesian Inference 1 and expe
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the world’s structured knowledge, and as a way to integrate information extracted from multiple data sources. Knowledge graphs have started to play a central role in representing the information extracted using natural language processing and computer vision. Domain knowledge expressed in KGs is being input into machine
We have added video introduction to some Stanford A.I. courses from Fall 2019 CS229. Please check them out at https://ai.stanford.edu/stanford-ai-courses
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There have been tremendous advances made in making machine learning more accessible over the past few years. Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems. In some cases these advancements have made it
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This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided. See the README file contained in the release for more details. Larg
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Introduction to Machine Learning Draft of Incomplete Notes by Nils J. Nilsson nilsson@cs.stanford.edu http://ai.stanford.edu/~nilsson Description (as of February 15, 2015): From this page you can download a draft of notes I used for a Stanford course on Machine Learning. Although I have tried to eliminate errors, some undoubtedly remain---caveat lector. Certain elements of the typography (overflow
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