4.6k citations · 7.9k across the 144 of their papers we have counts for
14 papers · 1 filter
Adaptive Quantization for Deep Neural Network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung +1
In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNN…
Graph Transform Optimization with Application to Image Compression
Giulia Fracastoro, Dorina Thanou, Pascal Frossard
In this paper, we propose a new graph-based transform and illustrate its potential application to signal compression. Our approach relies on the careful design of a graph that opti…
Graph Signal Processing: Overview, Challenges and Applications
Antonio Ortega, Pascal Frossard, Jelena Kovačević +2
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in…
Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance propert…
Online Resource Inference in Network Utility Maximization Problems
Stefano D'Aronco, Pascal Frossard
The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guara…
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such a…