Relational reasoning is a central component of generally intelligent behavior, but has
proven difficult for neural networks to learn. In this paper we describe how to use Relation …

Despite recent breakthroughs in the applications of deep neural networks, one setting that
presents a persistent challenge is that of" one-shot learning." Traditional gradient-based …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in
key domains such as vision, language, control, and decision-making. This has been due, in …

During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …

Large transformer-based models are able to perform in-context few-shot learning, without
being explicitly trained for it. This observation raises the question: what aspects of the training …

Memory-based neural networks model temporal data by leveraging an ability to remember
information for long periods. It is unclear, however, whether they also have an ability to …

The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The
recent success of deep networks in machine learning and AI, however, has inspired …

We introduce an approach for deep reinforcement learning (RL) that improves upon the
efficiency, generalization capacity, and interpretability of conventional approaches through …

We introduce hyperbolic attention networks to endow neural networks with enough capacity
to match the complexity of data with hierarchical and power-law structure. A few recent …