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Meta-Learning

106 papers with code ยท Methodology

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

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Latest papers without code

Few-Shot Learning with Global Class Representations

14 Aug 2019

In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples.

FEW-SHOT LEARNING META-LEARNING

Meta Reasoning over Knowledge Graphs

13 Aug 2019

The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.

FEW-SHOT LEARNING KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS META-LEARNING

Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction

12 Aug 2019

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction.

META-LEARNING

Optimizing quantum heuristics with meta-learning

8 Aug 2019

Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation.

META-LEARNING

Learning to Generalize to Unseen Tasks with Bilevel Optimization

5 Aug 2019

To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme.

META-LEARNING

Meta-Learning Improves Lifelong Relation Extraction

WS 2019

Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations.

META-LEARNING RELATION EXTRACTION

Few-Shot Meta-Denoising

31 Jul 2019

A solution to mitigate the small training set issue is to train a denoising model with pairs of clean and synthesized noisy signals, produced from empirical noise priors; and finally only fine-tune on the available small training set.

DENOISING FEW-SHOT LEARNING META-LEARNING TRANSFER LEARNING

Meta Learning for Task-Driven Video Summarization

29 Jul 2019

Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote generalization ability of the trained model.

META-LEARNING VIDEO SUMMARIZATION

ROAM: Recurrently Optimizing Tracking Model

28 Jul 2019

In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of the object at different positions and the second part regresses the relative bounding box shifts to anchors mounted on sliding-window locations.

META-LEARNING

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

27 Jul 2019

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning.

CALIBRATION FEW-SHOT LEARNING META-LEARNING