Questions tagged [meta-learning]

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MAML applied to unknown tasks in Meta-RL paper

I have been reviewing a paper on Meta-RL applied to Non-Stationary (NS) environment (Paper on arxiv), which assume that in a certain context of interest NS may be modeled as a switching environment ...
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Stacking ensembles in meta learning with only one base algorithm

I'm learning stacking ensembles in meta learning and , there is an example where thy used only lightgbm as base model and linear regression as meta model, they first Split thé dataset into 50 samples ...
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Strong bias from Linear SVR meta model

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Why would a Linear SVR model greatly outperform a Linear Regression model on model stacking

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Why do I get an almost perfect fit as well as bias variance tradeoff with my time series forecast?

In order to achieve scalable and robust time series forecast models, I am currently experimenting with metalearner ensembles. Note, that I am also using a global modeling approach, so all time series ...
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what about differences between the meta and semi-supervised and self-supervised and active and federated and few-shot learning?

what about difference between the meta learning and semi-supervised learning and self-supervised learning and active learning and federated learning and few-shot learning? in application and in ...
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Is ensemble learning a subset of meta learning?

I'm studying ensemble learning methods, focusing on random forest and gradient boost. I read this article about this topic and this about meta-learning. It is possible to say that ensemble learning is ...
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Can we consider Meta-features of a datasets as its embeddings?

While reading some works on meta-learning. I had this doubt. Can we consider meta-features of a dataset as it's embedding ? Given the meta-feature is a lower dimensional representation which also try ...
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How to use efficient net as feature extractor for meta/Few shot learning in PyTorch

I am working on few shot learning and I wanted to use efficient-net as backbone feature extractor. Most of the model use Resnet as feature extractor. For example I can use below line of code and it ...
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Is this a task of meta-learning or transfer learning?

I have a task that I am not able to identify if it is of transfer or meta learning. I want to know this, in order to ask help in solving it, because there are some parts that I have not understood. ...
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Siamese vs matching network for correct image category matching

I have to find the closest match between my image and bunch of already collected images of different classes in the folder. Whic meta-learning approach should I select. I am thinking about the Siamese ...
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Stacking - Appropriate base and meta models

When implementing stacking for model building and prediction (For example using sklearn's StackingRegressor function) what is the appropriate choice of models for the base models and final meta model?...
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How to implement my own loss function for Prototype learning using Keras Model

I'm trying to migrate this code, "Omniglot Character Set Classification Using Prototypical Network", into Tensorflow 2.1.0 and Keras 2.3.1. My problem is about how to use euclidean distance between ...
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Meta Learning: how to train a model with Support Set and Query Set

I've just started to learn Meta Learning reading the book Hands-On Meta Learning with Python. I think I know the answer for my question, but I'm a little confuse about how to implement the algorithm ...
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How to optimize hyperparameters in stacked model?

I was wondering whether somebody could explain how to optimize hyperparameters for the base learners and meta algorithm when stacking? In many tutorials they seem to be plucked out of thin air! ...
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Automatically uses several cores on R

I am using a library called MFE to generate meta-features. However, I am working right now with several files and I notice that I am using only 1 core of my machine and taking too much time. I have ...
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Are there any Meta Knowledge bank available?

What resources do you use to learn meta knowledge ? By meta knowledge, I mean generalized information that will help us take more informed decisions when working on a problem later. Example of meta ...
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How to search for an optimal dithering pattern?

I'm trying to find an optimal dithering pattern which can be used as a threshold on a greyscale image to generate a 1 bit black and white image. Ideally it would be optimal in the sense that a human ...
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How to feed the input to a Memory Augmented Neural Network (MAAN) to do one shot learning?

In this paper by Deep-Mind on one shot learning they have published an architecture explaining how the system works with an external meory. I understand the mechanism perfectly. But what I don't ...
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Isn't the optimizer network in deepminds learning to learn a DRQN?

In the paper "Learning to learn by gradient descent by gradient descent" they describe an RNN which learns gradient transformation to learn an optimizer. The optimizer network directly interacts ...
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Can clustering my data first help me learn better classifiers?

I was thinking about this lately. Let's say that we have a very complex space, which makes it hard to learn a classifier that can efficiently split it. But what if this very complex space is actually ...
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