6 votes

What are the differences between transfer learning and meta learning?

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 ...
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  • 317
5 votes
Accepted

What are the differences between transfer learning and meta learning?

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 ...
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  • 33k
5 votes
Accepted

Can we optimize an optimization algorithm?

First, you need to consider what are the "parameters" of this "optimization algorithm" that you want to "optimize". Let's take the most simple case, a SGD without momentum. The update rule for this ...
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  • 3,083
3 votes
Accepted

What are the state-of-the-art meta-reinforcement learning methods?

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 ...
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  • 371
3 votes

Do genetic algorithms also evolve?

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 (...
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  • 33k
3 votes

How does rotating an image and adding new 'rotated classes' prevent overfitting?

How can data augmentation reduce overfitting? You write that you can already maybe see how data augmentation can help prevent overfitting in general, but it sounds a bit uncertain and it's still ...
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  • 9,316
3 votes

How important is learning to learn for the development of AGI?

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 ...
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  • 33k
3 votes

What are the differences between transfer learning and meta learning?

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 $p(\tau)$. ...
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2 votes
Accepted

What is the difference between meta-learning and zero-shot learning?

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 ...
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  • 1,663
2 votes
Accepted

What are the features get from a feature extraction using a CNN?

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 ...
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  • 3,083
2 votes

Can we optimize an optimization algorithm?

We usually optimize with respect to something. For example, you can train a neural network to locate cats in an image. This operation of locating cats in an image can be thought of as a function: ...
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  • 33k
2 votes

How to split data for meta-learning?

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 ...
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  • 131
2 votes

Understanding the derivation of the first-order model-agnostic meta-learning

$\nabla_{\theta_{i-1}} \theta_{i-1} = \mathbf{I}$ 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 $...
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  • 33k
1 vote

What are recent AI software systems and research papers close to J. Pitrat's ideas?

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 ...
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1 vote

What AI conferences in Europe should I consider submitting papers to explaining the ongoing work on RefPerSys?

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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  • 111
1 vote

What are the state-of-the-art meta-reinforcement learning methods?

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 ...
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1 vote

What does "episodic training" mean?

It consists of organizing training in a series of learning problems, each relying on small "support" and "query" sets to mimic the few-shot circumstances encountered during ...
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1 vote

Why not go another layer deeper with Auto-AutoML?

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 ...
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1 vote

What is the internal state of a Simple Neural Attentive Meta-Learner(SNAIL)?

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 ...
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  • 431
1 vote

Do genetic algorithms also evolve?

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 ...
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