192 citations · 731 across the 30 of their papers we have counts for
6 papers · 1 filter
Federated Learning Based on Dynamic Regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro +3
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen device…
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
Shyam A. Tailor, René de Jong, Tiago Azevedo +2
In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a va…
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration
Zhi-Gang Liu, Paul N. Whatmough, Yuhao Zhu +1
Exploiting sparsity is a key technique in accelerating quantized convolutional neural network (CNN) inference on mobile devices. Prior sparse CNN accelerators largely exploit un-st…
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Urmish Thakker, Paul N. Whatmough, Zhigang Liu +2
Structured matrices, such as those derived from Kronecker products (KP), are effective at compressing neural networks, but can lead to unacceptable accuracy loss when applied to la…
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc, Partha Maji, Matthew Mattina +1
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quanti…
Information contraction in noisy binary neural networks and its implications
Chuteng Zhou, Quntao Zhuang, Matthew Mattina +1
Neural networks have gained importance as the machine learning models that achieve state-of-the-art performance on large-scale image classification, object detection and natural la…