Neural Processes (NPs)(Garnelo et al 2018a; b) approach regression by learning to map a
context set of observed input-output pairs to a distribution over regression functions. Each …

Inferring the structure of 3D scenes from 2D observations is a fundamental challenge in
computer vision. Recently popularized approaches based on neural scene representations …

Neural Processes combine the strengths of neural networks and Gaussian processes to
achieve both flexible learning and fast prediction in stochastic processes. However, a large …

In popular media, there is often a connection drawn between the advent of awareness in
artificial agents and those same agents simultaneously achieving human or superhuman …

The problem of inferring object shape from a single 2D image is underconstrained. Prior
knowledge about what objects are plausible can help, but even given such prior knowledge …

When tasks change over time, meta-transfer learning seeks to improve the efficiency of
learning a new task via both meta-learning and transfer-learning. While the standard …

Episodic memory plays a crucial role in various cognitive processes, such as the ability to
mentally recall past events. While cognitive science emphasizes the significance of spatial …

Active localization consists of generating robot actions that allow it to maximally
disambiguate its pose within a reference map. Traditional approaches use an information …

New approaches to 3D vision are enabling new advances in artificial intelligence and
autonomous vehicles, a better understanding of how animals navigate the 3D world, and …

We present a technique for adaptively partitioning neural scene representations. Our
method disentangles lighting, material, and geometric information yielding a scene …