In this paper we delve into the nuanced challenge of tailoring the Segment Anything Models
(SAMs) for integration with event data with the overarching objective of attaining robust and …

We demonstrate that deep ensembles are secretly equivariant models. More precisely, we
show that deep ensembles become equivariant for all inputs and at all training times by …

The quest for robust and generalizable machine learning models has driven recent interest
in exploiting symmetries through equivariant neural networks. In the context of PDE solvers …

Equivariant neural networks have been widely used in a variety of applications due to their
ability to generalize well in tasks where the underlying data symmetries are known. Despite …

We consider achieving equivariance in machine learning systems via frame averaging.
Current frame averaging methods involve a costly sum over large frames or rely on sampling …

Medical imaging is crucial for diagnosing a patient's health condition, and accurate
segmentation of these images is essential for isolating regions of interest to ensure precise …

Equivariance builds known symmetries into neural networks, often improving generalization.
However, equivariant networks cannot break self-symmetries present in any given input …

Generative modeling of symmetric densities has a range of applications in AI for science,
from drug discovery to physics simulations. The existing generative modeling paradigm for …

We propose SymDiff, a novel method for constructing equivariant diffusion models using the
recently introduced framework of stochastic symmetrisation. SymDiff resembles a learned …

Deep neural networks are applied in more and more areas of everyday life. However, they
still lack essential abilities, such as robustly dealing with spatially transformed input signals …