Breakthrough Technique: Meta-learning for Compositionality
Original :
https://www.nature.com/articles/s41586-023-06668-3
Vulgarization :
https://scitechdaily.com/the-future-of-machine-learning-a-new-breakthrough-technique/
How MLC Works
In exploring the possibility of bolstering compositional learning in neural networks, the researchers created MLC, a novel learning procedure in which a neural network is continuously updated to improve its skills over a series of episodes. In an episode, MLC receives a new word and is asked to use it compositionally—for instance, to take the word “jump” and then create new word combinations, such as “jump twice” or “jump around right twice.” MLC then receives a new episode that features a different word, and so on, each time improving the network’s compositional skills.
What is meant here (and said as such in the article) is that humans can learn from a single example while deep neural networks takes thousands or millions (of examples) to learn.
Ok, but neural networks can process way more examples per second so ‘faster’ is not really the right term here.
Yes you are right. And I was hoping for someone more knowledgeable to help clarify this topic.
Well I was lucky with the comment of
@DigitalMusin here, if you would like to read it.