![]() In standard DL contexts, learning occurs over hundreds of thousands or even millions of gradient steps, but generally, “learning'' can also occur on shorter (conditioning) or longer timescales (hyperparameter search). A common learning operator used in deep learning and reinforcement learning literature is the stochastic gradient descent algorithm, with respect to a loss function. We define a learning operator $f : F_\theta \to F_\theta$ as a function that improves a model function $f_\theta$ with respect to some task. Feel free to skip to the next section if you want to dive straight into the MAML+JAX coding tutorial. This section is my attempt to make the definition of “learning” and “meta-learning” more mathematically precise, and explain why seemingly different algorithms are all branded as “meta-learning” these days. A source of this confusion stems from the blurred semantics between “optimization”, “learning”, “adaptation”, “memory”, and how these terms can be employed in wildly different applications. “Meta-learning” is used in so many different research contexts nowadays that it's difficult to communicate to other researchers what I’m exactly working on when I say “Meta-Learning”. Specifically, I'll show you how to implement the MAML meta-learning algorithm in about 50 lines of Python code, using Google's awesome JAX library. This blog post won’t cover all the possible ways in which one can build a meta-learning system instead, this is a practical tutorial on how to get your feet wet in meta-learning research. Although Meta-Learning has attracted a lot of research attention in recent years, related ideas and algorithms have been around for some time (see Hugo Larochelle's slides and Lilian Weng’s blog post for an excellent overview of related concepts). “Meta-learning'', one of the most exciting ML research topics right now, addresses this problem by optimizing a model not just for the ability to “predict well'', but also the ability to “learn well''. Can we build systems that can learn faster, and with less data? correcting mistakes) without an expensive retraining process. This learning procedure is time-consuming and once a deep model is trained, its behavior is fairly rigid at deployment time, one cannot really change the behavior of the system (e.g. However, what most ML researchers call “learning” right now is but a very small subset of the vast range of behavioral adaptability encountered in biological life! Deep Learning models are powerful, but require a large amount of data and many iterations of stochastic gradient descent (SGD). The focus of Machine Learning (ML) is to imbue computers with the ability to learn from data, so that they may accomplish tasks that humans have difficulty expressing in pure code. Biological intelligence blurs the boundaries between “ behavior” (responding to the environment), “ learning” (acquiring information about the world in order to improve fitness), and “optimization” ( improving fitness). At the shortest of timescales, a single ion channel activating in response to a stimulus can also be thought of as “learning”, as it is an adaptive, stateful response to the environment. ![]() At the longest time-scales, evolution itself can be thought of as “learning” on the genomic level, whereby favorable genetic codes are discovered and remembered over the course of many generations. Multi-cellular developmental programs are highly plastic and can even store epigenetic “memories'” between generations. Learning is hardly restricted to animal-level intelligence it can be found in every living creature. More difficult skills, such as mastering a musical instrument, are acquired over a lifetime of deliberate practice. Upon reading a news article, I obtain new information that I didn't have before. Adaptive behavior in humans and animals occurs at many time scales: when I use a new shower handle for the first time, it takes me a few seconds to figure out how to adjust the water temperature to my liking.
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