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From Learning to Meta-Learning: Reduced Training Overhead and Complexity
  for Communication Systems

From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems

5 January 2020
Osvaldo Simeone
Sangwoo Park
Joonhyuk Kang
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems"

13 / 13 papers shown
Title
FPGA Implementation of Multi-Layer Machine Learning Equalizer with
  On-Chip Training
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
Keren Liu
E. Börjeson
Christian Hager
P. Larsson-Edefors
68
4
0
07 Dec 2022
Online Bayesian Meta-Learning for Cognitive Tracking Radar
Online Bayesian Meta-Learning for Cognitive Tracking Radar
C. Thornton
R. M. Buehrer
A. Martone
57
6
0
07 Jul 2022
Robust Bayesian Learning for Reliable Wireless AI: Framework and
  Applications
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
Matteo Zecchin
Sangwoo Park
Osvaldo Simeone
Marios Kountouris
David Gesbert
78
15
0
01 Jul 2022
An Energy and Carbon Footprint Analysis of Distributed and Federated
  Learning
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
S. Savazzi
V. Rampa
Sanaz Kianoush
M. Bennis
69
45
0
21 Jun 2022
Modular Meta-Learning for Power Control via Random Edge Graph Neural
  Networks
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks
I. Nikoloska
Osvaldo Simeone
79
22
0
04 Aug 2021
Bayesian Active Meta-Learning for Few Pilot Demodulation and
  Equalization
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization
K. Cohen
Sangwoo Park
Osvaldo Simeone
S. Shamai
98
12
0
02 Aug 2021
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph
  Neural Networks
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
I. Nikoloska
Osvaldo Simeone
102
21
0
02 May 2021
Solving Stochastic Compositional Optimization is Nearly as Easy as
  Solving Stochastic Optimization
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Tianyi Chen
Yuejiao Sun
W. Yin
139
82
0
25 Aug 2020
Team Deep Mixture of Experts for Distributed Power Control
Team Deep Mixture of Experts for Distributed Power Control
Matteo Zecchin
David Gesbert
Marios Kountouris
126
4
0
28 Jul 2020
Information-Theoretic Generalization Bounds for Meta-Learning and
  Applications
Information-Theoretic Generalization Bounds for Meta-Learning and Applications
Sharu Theresa Jose
Osvaldo Simeone
76
47
0
09 May 2020
End-to-End Fast Training of Communication Links Without a Channel Model
  via Online Meta-Learning
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
Sangwoo Park
Osvaldo Simeone
Joonhyuk Kang
95
42
0
03 Mar 2020
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO
  Detection
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
Nir Shlezinger
Rong Fu
Yonina C. Eldar
95
103
0
08 Feb 2020
perm2vec: Graph Permutation Selection for Decoding of Error Correction
  Codes using Self-Attention
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Nir Raviv
Avi Caciularu
Tomer Raviv
Jacob Goldberger
Yair Be’ery
63
8
0
06 Feb 2020
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