327 citations · 357 across the 20 of their papers we have counts for
5 papers · 1 filter
Fast and Differentiable Message Passing on Pairwise Markov Random Fields
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-cra…
Mirror Descent View for Neural Network Quantization
Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr +2
Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. It is usually fo…
A Signal Propagation Perspective for Pruning Neural Networks at Initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould +1
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while mi…
Learning to Adapt for Stereo
Alessio Tonioni, Oscar Rahnama, Thomas Joy +3
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are succe…
On Tiny Episodic Memories in Continual Learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny +4
In continual learning (CL), an agent learns from a stream of tasks leveraging prior experience to transfer knowledge to future tasks. It is an ideal framework to decrease the amoun…