79 citations · 154 across the 5 of their papers we have counts for
5 papers
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order…
Learning a Representation Map for Robot Navigation using Deep Variational Autoencoder
Kaixin Hu, Peter O'Connor
The aim of this work is to use Variational Autoencoder (VAE) to learn a representation of an indoor environment that can be used for robot navigation. We use images extracted from…
Temporally Efficient Deep Learning with Spikes
Peter O'Connor, Efstratios Gavves, Max Welling
The vast majority of natural sensory data is temporally redundant. Video frames or audio samples which are sampled at nearby points in time tend to have similar values. Typically,…
Sigma Delta Quantized Networks
Peter O'Connor, Max Welling
Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity…
Deep Spiking Networks
Peter O'Connor, Max Welling
We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time,…