# Tag Info

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Meta-learning is more about speeding up and optimizing hyperparameters for networks that are not trained at all, whereas transfer learning uses a net that has already been trained for some task and reusing part or all of that network to train on a new task which is relatively similar. So, although they can both be used from task to task to a certain degree, ...

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One of the most recent papers on meta-RL is meta-Q-learning This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-reinforcement learning (meta-RL). MQL builds upon three simple ideas. Q-learning is competitive with state of the art meta-RL algorithms if given access to a context variable that is a representation of the past ...

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Learning to learn (also known as meta-learning) is very important for the development of artificial general intelligence (AGI), given that one of the desirable and fundamental properties of an AGI is the adaptability to different environments and the ability to continually learn, and meta-learning can be used to achieve that. Meta-learning is thus related ...

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You get what we call high-level features, which are basically abstract representations of the parts that carry information in the image you want to classify. Imagine you want to classify a car. The image you feed your network could be a car on a road with a driver and trees and clouds, etc. The network, however, if you've trained it to recognize cars, will ...

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The difference really comes down to the fact that in meta-learning, there is a population of tasks $\tau$ which have distribution $p(\tau)$. The goal is to perform well on a task drawn from $p(\tau)$. Generally 'perform well' means that with only a few training steps or data points, the model can give good classification accuracy, achieve high reward in an ...

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First see the definition of meta-learning: Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017 the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in ...

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$\nabla_{\theta_{i-1}} \theta_{i-1} = \mathbf{I}$ in a similar way that $\frac{d f}{dx} = 1$ for $f(x) = x$. Strictly speaking, $\mathbf{I}$ should be a vector of $1s$ with the same dimensionality as $\theta_{i-1}$, but they are probably abusing notation here and putting such a vector as the diagonal elements of a matrix. Alternatively (actually, the most ...

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Probably to as many as possible. Average accept rate of papers is around 20%. You can find the best conferences on AI & ML Event.

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First of all, I would like to say that it is possible that these terms are used inconsistently, given that at least transfer learning, AFAIK, is a relatively new expression, so, the general trick is to take terminology, notation and definitions with a grain of salt. However, in this case, although it may sound confusing to you, all of the current ...

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Logically it is possible, but you will just end up complicating the entire task. The aim of AutoML is to provide a drop in solution to the customers. To do this, a trained network decides and generates the model architecture. This is done so that anyone with basic experience is able to integrate the solution into their systems. Currently, the complicated ...

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Here's what I understand, welcome to point out any mistakes. When starting a new episode(but still in the same task), SNAIL does not clear its batches. Instead, it makes decisions based on the current observation and observation-action pairs from the previous episode. In this way, it keeps knowledge of the previous episode whereby achieving few-shot ...

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