Compact and Computationally Efficient Representation of Deep Neural Networks

27 May 2018

Papers citing "Compact and Computationally Efficient Representation of Deep Neural Networks"

11 / 11 papers shown

Title
Efficient Adaptive Federated Optimization of Federated Learning for IoT Zunming Chen Hongyan Cui Ensen Wu Yu Xi 27 0 0 23 Jun 2022
Compacting Deep Neural Networks for Internet of Things: Methods and Applications Ke Zhang Hanbo Ying Hongning Dai Lin Li Yuangyuang Peng Keyi Guo Hongfang Yu 16 38 0 20 Mar 2021
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons Simon Wiedemann Suhas Shivapakash P. Wiedemann Daniel Becking Wojciech Samek F. Gerfers Thomas Wiegand MQ 16 7 0 17 Dec 2020
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification Negar Heidari Alexandros Iosifidis GNN 16 14 0 27 Mar 2020
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints Felix Sattler K. Müller Wojciech Samek FedML 40 965 0 04 Oct 2019
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks Simon Wiedemann H. Kirchhoffer Stefan Matlage Paul Haase Arturo Marbán ... Ahmed Osman D. Marpe H. Schwarz Thomas Wiegand Wojciech Samek 41 92 0 27 Jul 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 16 1,329 0 07 Mar 2019
Multi-Kernel Prediction Networks for Denoising of Burst Images Talmaj Marinc Vignesh Srinivasan Serhan Gül C. Hellge Wojciech Samek 20 26 0 05 Feb 2019
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication Felix Sattler Simon Wiedemann K. Müller Wojciech Samek MQ 11 210 0 22 May 2018
Universal Deep Neural Network Compression Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 81 85 0 07 Feb 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017