Knowledge Distillation Applied to Optical Channel Equalization: Solving
the Parallelization Problem of Recurrent Connection

Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

8 December 2022

S. Srivallapanondh

Pedro J. Freire

Jaroslaw E. Prilepsky

Papers citing "Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection"

5 / 5 papers shown

Title
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation Pedro J. Freire A. Napoli D. A. Ron B. Spinnler M. Anderson W. Schairer T. Bex N. Costa S. Turitsyn Jaroslaw E. Prilepsky 45 29 0 26 Aug 2022
Performance and Complexity Analysis of bi-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systems S. Deligiannidis C. Mesaritakis Adonis Bogris 31 45 0 03 Mar 2021
Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks S. Deligiannidis Adonis Bogris C. Mesaritakis Y. Kopsinis 18 69 0 31 Jan 2020
WaveNet: A Generative Model for Raw Audio Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner A. Senior Koray Kavukcuoglu DiffM 196 7,361 0 12 Sep 2016
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 69 19,448 0 09 Mar 2015