14 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
Induced Feature Selection by Structured Pruning
Nathan Hubens, Victor Delvigne, Matei Mancas +3
The advent of sparsity inducing techniques in neural networks has been of a great help in the last few years. Indeed, those methods allowed to find lighter and faster networks, abl…
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
Neural network pruning is a widely used strategy for reducing model storage and computing requirements. It allows to lower the complexity of the network by introducing sparsity in…
An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely h…
One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
Introducing sparsity in a neural network has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced us…