348 citations · 1.5k across the 43 of their papers we have counts for
9 papers · 1 filter
VoronoiNet: General Functional Approximators with Local Support
Francis Williams, Daniele Panozzo, Kwang Moo Yi +1
Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it -- via a novel…
Reducing the Human Effort in Developing PET-CT Registration
Teaghan O'Briain, Kyong Hwan Jin, Hongyoon Choi +3
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspond…
Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework
Spencer Bialek, Sébastien Fabbro, Kim A. Venn +3
In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we…
LRP2020: Machine Learning Advantages in Canadian Astrophysics
K. A. Venn, S. Fabbro, A Liu +12
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more po…
Optimizing Through Learned Errors for Accurate Sports Field Registration
Wei Jiang, Juan Camilo Gamboa Higuera, Baptiste Angles +3
We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Ins…
Beyond Cartesian Representations for Local Descriptors
Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi +2
The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if…
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Weiwei Sun, Wei Jiang, Eduard Trulls +2
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution-they opera…
Self-Supervised Deep Active Accelerated MRI
Kyong Hwan Jin, Michael Unser, Kwang Moo Yi
We propose to simultaneously learn to sample and reconstruct magnetic resonance images (MRI) to maximize the reconstruction quality given a limited sample budget, in a self-supervi…
Linearized Multi-Sampling for Differentiable Image Transformation
Wei Jiang, Weiwei Sun, Andrea Tagliasacchi +2
We propose a novel image sampling method for differentiable image transformation in deep neural networks. The sampling schemes currently used in deep learning, such as Spatial Tran…