Meta learning (computer science) - Wikipedia.
Meta-learning is a relatively new direction in the field of artificial intelligence and is considered to be the key to realizing general artificial intelligence. Why is he so important? How to quickly and easily understand the essence of meta-learning? This article will introduce you to the meta-learning in detail.
Meta-Learning controllers of Curriculum Learning look at how data is ranked according to some metric of perceived difficulty and the order in which this data should be presented in. A recent study from Hacohen and Weinshall presents interesting success with this (shown below) in the ICML 2019 conference.
Think meta-learning. Think Tim Ferriss. Specifically, this book: The 4-Hour Chef: The Simple Path to Cooking Like a Pro, Learning Anything, and Living the Good Life WHAT IF YOU COULD BECOME WORLD-CLASS IN ANYTHING IN 6 MONTHS OR LESS? The 4-Hour C.
Meta-Learning fosters the process of self- re6lection and learning how to learn, as well as the building of the other three dimensions. To learn more about the work and focus of the Center for Curriculum Redesign, please visit our website at.
Chelsea Finn cbfinn at cs dot stanford dot edu I am an Assistant Professor in Computer Science and Electrical Engineering at Stanford University.My lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group.I also spend time at Google as a part of the Google Brain team. I am interested in the capability of robots and other.
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Abstract: Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better initialisations or scaling factors for a gradient-based update rule.