šš¦ Edge#73: Meta-Learning and AutoML, OpenAIās Reptile Model, and the Auto-Keras Framework
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š”Ā ML Concept of the Day:Ā Meta-Learning and AutoML
In the final issue of our series about AutoML, we would like to discuss the perspective of meta-learning as a form of AutoML. This position is a bit controversial because, conceptually, meta-learning encompasses a bigger universe of techniques focused on ālearning to learn.ā However,Ā many meta-learning methods end up generating new machine learning models for a given task, which is a clear definition of AutoML.Ā
Conceptually, meta-learning typically refers to the ability of a model to improve the learning of sophisticated tasks by reusing knowledge learned in previous tasks.āÆThat level of knowledge acquisition and reusability can be foundational in many AutoML methods. While there is a very broad set of meta-learning methods, we can identify three main forms that are relevant to AutoML:Ā Meta-Learning Methods Based on Model Evaluations, Meta-Learning Methods Based on Task Properties, Meta-Learning Methods Based on Previous Models
šĀ ML Research You Should Know:Ā OpenAIās Reptile is One of the Most Efficient Meta-Learning Methods Ever CreatedĀ
In their paper, researchers from OpenAI propose a super clever meta-learning algorithm called Reptile that can quickly learn new tasks from a given distribution.Ā
When I first read about Reptile, I was surprised that this method worked at all. At first glance, Reptile seems like the type of meta-learning model that could only work in problems where zero-shot learning is possible, as it basically relies on performing SGD on a mixture of tasks. Howeverā¦ ->continue reading to stay in the know of the most relevant AI&ML papers
š¤Ā ML Technology to Follow: Auto-Keras is an AutoML Framework Every Data Scientists Should Know
Auto-Keras is one of the simplest and most widely-used AutoML libraries in the data science space.Ā
Differently from other AutoML frameworks in the market, Auto-Keras focuses on deep learning tasks. Its architecture is based on five fundamental componentsĀ ā>learn more
š§ The Quiz
Now, to our regular quiz. We reward the winners every ten quizzes. The questions are the following:
Which of the following statementsĀ best describesĀ OpenAIāsĀ Reptile meta-learning algorithm?
What is the main innovation of Auto-Keras compared to other NAS or AutoML stacks?
That was fun! š Thank you.Ā
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