374 citations · 394 across the 7 of their papers we have counts for
6 papers · 1 filter
Are LSTMs Good Few-Shot Learners?
Mike Huisman, Thomas M. Moerland, Aske Plaat +1
Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by…
Subspace Adaptation Prior for Few-Shot Learning
Mike Huisman, Aske Plaat, Jan N. van Rijn
Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent…
Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques
Mike Huisman, Aske Plaat, Jan N. van Rijn
Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approac…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…
Stateless Neural Meta-Learning using Second-Order Gradients
Mike Huisman, Aske Plaat, Jan N. van Rijn
Deep learning typically requires large data sets and much compute power for each new problem that is learned. Meta-learning can be used to learn a good prior that facilitates quick…
A Survey of Deep Meta-Learning
Mike Huisman, Jan N. van Rijn, Aske Plaat
Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is…