no code implementations • 24 Apr 2019 • Jeff Murugan, Duncan Robertson
We close the article with a discussion of the resilience of topological data analysis to noise and some statistical and computational challenges faced by the method.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Theory
no code implementations • CVPR 2017 • Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi
We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.
no code implementations • CVPR 2016 • Sukrit Shankar, Duncan Robertson, Yani Ioannou, Antonio Criminisi, Roberto Cipolla
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks.
1 code implementation • 3 Mar 2016 • Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
no code implementations • 20 Nov 2015 • Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi
Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.