Recent years have seen a lot of interest in the use and development of learning-to-learn algorithms. Research on learning-to-learn, or meta-learning, algorithms is often motivated by the hope to learn representations that can be easily transferred to the learning of new skills, and lead to faster learning. Yet, current meta-learned representations often struggle to generalize to novel task settings. In this workshop, we’d like to discuss how humans meta-learn, and what we can and should expect from learning-to-learn in the field of machine learning. Our aim is to bring together researchers from a variety of backgrounds with the hope to discuss and reason about what learning to learn means from a cognitive perspective, and how this knowledge might translate into algorithmic advances. In particular we are interested in creating a platform to enable the exchange between the fields of neuroscience and machine learning.
We believe that it is an important moment for the machine learning community to reflect upon these questions in order to advance the field and increase its variety in approaching learning to learn. We hope that by fostering discussions between cognitive science and machine learning researchers, we enable both sides to draw inspiration to further the understanding and development of learning-to-learn algorithms.