In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

Automatically discovering composable abstractions from raw perceptual data is a long-
standing challenge in machine learning. Recent slot-based neural networks that learn about …

Learning visual features from unlabeled images has proven successful for semantic
categorization, often by mapping different views of the same object to the same feature to …

Abstraction has been widely studied as a way to improve the efficiency and generalization of
reinforcement learning algorithms. In this paper, we study abstraction in the continuous …

Extensive work has demonstrated that equivariant neural networks can significantly improve
sample efficiency and generalization by enforcing an inductive bias in the network …

Predicting the pose of objects from a single image is an important but difficult computer
vision problem. Methods that predict a single point estimate do not predict the pose of …

We introduce a general method for learning representations that are equivariant to
symmetries of data. Our central idea is to decompose the latent space into an invariant factor …

How can agents learn internal models that veridically represent interactions with the real
world is a largely open question. As machine learning is moving towards representations …

Although equivariant machine learning has proven effective at many tasks, success
depends heavily on the assumption that the ground truth function is symmetric over the …

As transformers are equivariant to the permutation of input tokens, encoding the positional
information of tokens is necessary for many tasks. However, since existing positional …