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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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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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In principle, yes, you can also evolve the genetic algorithm (or, in general, evolutionary algorithm), i.e. you can evolve its operations (such as the mutation and cross-over) and hyper-parameters (such as the size of the population or mutation rate). For example, you could use genetic programming to evolve the cross-over operation of a genetic algorithm. ...


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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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$\nabla_{\theta_{i-1}} \theta_{i-1} = \mathbf{I}$ 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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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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I assume in your case what you need to be doing is to collate your 3 datasets together - these would form the training dataset, and then leave the testing dataset aside. During Meta-Training, the code will sample a batch of tasks in each iteration. This batch of tasks will be split into support and query, the algorithm will train on the support and update ...


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Today one of the challenges is learning representations/concepts that are causally invariant. Once we have good representations then we can work on the reasoning aspect. There are 2 camps of people today. One believes that symbolic manipulation cannot be achieved properly by deep networks. Hence, they separate the task of extracting a lower-dimensional ...


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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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Meta-Reinforcement Learning can refer to a broad range of ideas. Also, different algorithms are SOTA under different evaluation metrics (sample efficiency, agent performance, adaptation speed on a new task, etc) Assuming that you are referring to the problem of quickly learning/adapting to a new task by training an agent on a distribution of related tasks, ...


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A genetic algorithm is a class of evolutionary algorithms. They do get better at searching through the solution possibilities for each trial (generation) over time because evolution usually starts from a population of randomly generated individuals, and is an iterative process. In each generation, the fitness of every individual in the population is ...


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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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