Reptile: A scalable meta-learning algorithm
•We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the...
•Reptile is the application of the Shortest Descent algorithm to the meta-learning setting, and is mathematically similar to first-order MAML (which is a version of the well-known MAML algorithm) that...
هذا الخبر من OpenAI Blog. خبر يقدم أدوات ذكاء اصطناعي للتلخيص والترجمة والاستماع.
We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. Reptile is the application of the Shortest Descent algorithm to the meta-learning setting, and is mathematically similar to first-order MAML (which is a version of the well-known MAML algorithm) that only needs black-box access to an optimizer such as SGD or Adam, with similar computational efficiency and performance.المصدر: OpenAI Blog | Source: OpenAI Blog
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This article was originally published by OpenAI Blog. Khabr is a licensed Jordanian AI-powered news platform (Registration #82086). We add editorial value through: AI-powered news analysis, automated summaries, AI audio narration, multi-language translation (Arabic, English, French, Turkish), and AI fact-checking. Our mission is to make news more accessible and understandable for Arabic-speaking audiences worldwide.
