32 citations · 200 across the 25 of their papers we have counts for
4 papers · 1 filter
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Owen Melia, Eric Jonas, Rebecca Willett
Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant ma…
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett +2
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence. These datasets open up the possibility of i…
Detection and Description of Change in Visual Streams
Davis Gilton, Ruotian Luo, Rebecca Willett +1
This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps. We propose a new approach t…
Neumann Networks for Inverse Problems in Imaging
Davis Gilton, Greg Ongie, Rebecca Willett
Many challenging image processing tasks can be described by an ill-posed linear inverse problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all…