14 citations · 21 across the 3 of their papers we have counts for
6 papers
WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving
Jannik Zürn, Paul Gladkov, Sofía Dudas +5
We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many…
Probabilistic Future Prediction for Video Scene Understanding
Anthony Hu, Fergal Cotter, Nikhil Mohan +2
We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scene…
A Learnable ScatterNet: Locally Invariant Convolutional Layers
Fergal Cotter, Nick Kingsbury
In this paper we explore tying together the ideas from Scattering Transforms and Convolutional Neural Networks (CNN) for Image Analysis by proposing a learnable ScatterNet. Previou…
A Framework for Implementing Machine Learning on Omics Data
Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter +4
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics…
Deep Learning in the Wavelet Domain
Fergal Cotter, Nick Kingsbury
This paper examines the possibility of, and the possible advantages to learning the filters of convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are…
Visualizing and Improving Scattering Networks
Fergal Cotter, Nick Kingsbury
Scattering Transforms (or ScatterNets) introduced by Mallat are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classifica…