The desire to reduce the dependence on curated, labeled datasets and to leverage the vast
quantities of unlabeled data has triggered renewed interest in unsupervised (or self …

Although the cognitive sciences aim to ultimately understand behavior and brain function in
the real world, for historical and practical reasons, the field has relied heavily on artificial …

Abstract 'Intuitive physics' enables our pragmatic engagement with the physical world and
forms a key component of 'common sense'aspects of thought. Current artificial intelligence …

Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …

Due to the inherent model uncertainty, learning to infer Bayesian posterior from a few-shot
dataset is an important step towards robust meta-learning. In this paper, we propose a novel …

The objective of this paper is few-shot object detection (FSOD)-the task of expanding an
object detector for a new category given only a few instances as training. We introduce a …

Metric-based meta-learning techniques have successfully been applied to few-shot
classification problems. In this paper, we propose to leverage cross-modal information to …

Deep learning models currently achieve human levels of performance on real-world face
recognition tasks. We review scientific progress in understanding human face processing …

Within months of birth, children develop meaningful expectations about the world around
them. How much of this early knowledge can be explained through generic learning …

Learning to infer Bayesian posterior from a few-shot dataset is an important step towards
robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we …