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

Image keypoint descriptions that are discriminative and matchable over large changes in
viewpoint are vital for 3D reconstruction. However descriptions output by learned descriptors …

Recent progress has been made towards learning invariant or equivariant representations
with self-supervised learning. While invariant methods are evaluated on large scale …

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

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 …

Incorporating geometric inductive biases into models can aid interpretability and
generalization, but encoding to a specific geometric structure can be challenging due to the …

Learning from previously collected datasets of expert data offers the promise of acquiring
robotic policies without unsafe and costly online explorations. However, a major challenge is …

Equivariant neural networks require explicit knowledge of the symmetry group. Automatic
symmetry discovery methods aim to relax this constraint and learn invariance and …

Cross-modal retrieval plays a key role in the Natural Language Processing area, which aims
to retrieve one modality to another efficiently. Despite the notable achievements of existing …

We address the problem of learning representations from observations of a scene involving
an agent and an external object the agent interacts with. To this end, we propose a …