59 citations · 110 across the 49 of their papers we have counts for
7 papers · 1 filter
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
Thomas M. Moerland, Matthias Müller-Brockhausen, Zhao Yang +7
Due to the empirical success of reinforcement learning, an increasing number of students study the subject. However, from our practical teaching experience, we see students enterin…
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…
Human-Robot Co-Creativity: A Scoping Review -- Informing a Research Agenda for Human-Robot Co-Creativity with Older Adults
Marianne Bossema, Somaya Ben Allouch, Aske Plaat +1
This review is the first step in a long-term research project exploring how social robotics and AI-generated content can contribute to the creative experiences of older adults, wit…
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Joost Broekens, Bernhard Hilpert, Suzan Verberne +3
Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore Chat…