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One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot). [ 1 ] In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, [ 24 ] and that it had been pre-published while waiting for completion of its review.
Blurred intentionally on Bing Maps. [15] Rendered in lower resolution on Google Maps and Mapquest. Heliport [16] in El Ejido: Spain: Square blurred on Google and Bing. Visible e.g. in HERE WeGo and Yandex.
Google Maps' location tracking is regarded by some as a threat to users' privacy, with Dylan Tweney of VentureBeat writing in August 2014 that "Google is probably logging your location, step by step, via Google Maps", and linked users to Google's location history map, which "lets you see the path you've traced for any given day that your ...
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Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Timothy P. Lillicrap is a Canadian neuroscientist and AI researcher, adjunct professor at University College London, and staff research scientist at Google DeepMind, where he has been involved in the AlphaGo and AlphaZero projects mastering the games of Go, Chess and Shogi.
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