Questions tagged [meta-learning]

For questions related to the concept of meta-learning (or learning-to-learn).

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How many tasks are needed for meta-learning?

This is an empirical question, essentially how many tasks do you need data for, to make a useful meta learning model (e.g. using MAML)? I'm looking for ranges based on personal experience or if anyone ...
profPlum's user avatar
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1 answer
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What exactly does meta-learning in reinforcement learning setting mean?

We can use DDPG to train agents to stack objects. And stacking objects can be viewed as first grasping followed by pick and place. In this context, how does meta-reinforcement learning fit? Does it ...
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What is the difference between "out-of-distribution (generalisation)" and "(meta)-transfer learning"?

I'm trying to develop a better understanding of the concept of "out-of-distribution" (generalization) in the context of Bengio's "Moving from System 1 DL to System 2 DL" and the concept of "(meta)-...
maxcompression's user avatar
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In few-shot classification, should I use my custom dataset as the validation dataset and mini-ImageNet as the training dataset?

I am new to few-shot learning, and I wanted to get a hands-on understanding of it, using Reptile algorithm, applied to my custom dataset. My custom dataset has 30 categories, with 5 images per ...
Geneveve08's user avatar
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How does recurrent neural network implement model based RL system purely in its activation dynamics (in blackbox meta-rl setting)?

I have read these papers "learning to reinforcement learn" and "PFC as meta RL system". The authors claim that when RNN is trained on multiple tasks from a task distribution using ...
veerendra's user avatar
1 vote
1 answer
588 views

How does MAML inner loop optimization works?

I started to learn meta-learning, reading the MAML paper https://arxiv.org/pdf/1703.03400.pdf In the inner loop, I am calculating adapted parameters for each task, I will be doing multiple steps of ...
Grumpy C's user avatar
1 vote
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Why doesn't anyone use reinforcement learning to find the best possible alternative to backpropagation?

To be clear, I'm very uninformed on the topic of alternative learning algorithms to backprop, all my knowledge comes from articles like these: lets-not-stop-at-backprop backprop-alternatives we-need-a-...
Ethan's user avatar
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What's mutual exclusivity in meta-learning?

What do we mean by mutual exclusivity of tasks? This work (E Pan, 21) and this one (M Yin, 20) state that most classification meta-learning algorithms fail for non-mutually exclusive tasks as the ...
mugoh's user avatar
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How do I format task features with a one-hot task identification vector to ensure separate weight matrices for each task in multi-task RL?

I am on Lecture 2 of Stanford CS330 Multi-Task and Meta-learning, and on slide 10, the professor describes using a one-hot input vector to represent the task, and she also explained that there would ...
iamPres's user avatar
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Which meta-learning algorithms are well-suited for "many-shot learning" scenarios, where the target training set is large?

Much of the meta-learning literature deals with the few-shot learning problem of using data from a diverse set of "source" tasks (the meta-dataset) in order to train a model that can quickly ...
Ori's user avatar
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In MAML for RL, are new tasks sampled for every meta update, or is the same set of tasks used throughout?

Consider Model Agnostic Meta-Learning, as described here. For a RL task $T_i$, represented with a model $f$, with parameters $\theta$ and learning rate $\alpha$, where the RL loss function is $\...
hazrmard's user avatar
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Episodic Learning vs. Meta Learning in few-shot setting

As far as I understand, episodic learning involves training the model on episodes of tasks, where each task consists of a query set Q and support set S. On each episode the model is trained on the ...
Damm Joe's user avatar
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28 views

What is the meaning of task distribution in the context of meta-learning?

I am working on meta-reinforcement learning and after reading the literature, I cannot clear myself on the meaning of task distribution. Please describe task distribution if you can rigorously!
Engr. Moiz Ahmad's user avatar
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Resolving Derivation Discrepancies for Differentiating through Optimization Paths

I'm reading the paper "Optimizing Millions of Hyperparameters by Implicit Differentiation". The key contribution of the paper is to show that you can replace optimizing through the ...
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What are practical methods to acquire a large number of tasks for Meta-learning?

It appears that it may be necessary to acquire a very large number of tasks for meta-learning , because MAML for example says that each task is analogous to a single training example in regular ...
profPlum's user avatar
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Which meta-learning approach selection methodology should I use for similarity learning of an image?

Meta-learning has 3 broad approaches: model, metric and optimization-based approach. Each of them has its own sub-approach, like matching network, meta-agonistic and Siamese-based network, and so on. ...
Rambo_john's user avatar