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Bayesian Active Meta-Learning for Few Pilot Demodulation and
  Equalization

Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization

2 August 2021
K. Cohen
Sangwoo Park
Osvaldo Simeone
S. Shamai
ArXivPDFHTML

Papers citing "Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization"

11 / 11 papers shown
Title
Leveraging Large Language Models for Wireless Symbol Detection via
  In-Context Learning
Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning
Momin Abbas
Koushik Kar
Tianyi Chen
21
5
0
28 Aug 2024
Towards Efficient and Trustworthy AI Through
  Hardware-Algorithm-Communication Co-Design
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design
Yongchao Chen
Osvaldo Simeone
Bashir M. Al-Hashimi
26
4
0
27 Sep 2023
Calibrating AI Models for Wireless Communications via Conformal
  Prediction
Calibrating AI Models for Wireless Communications via Conformal Prediction
K. Cohen
Sangwoo Park
Osvaldo Simeone
S. Shamai
24
6
0
15 Dec 2022
Machine Learning with a Reject Option: A survey
Machine Learning with a Reject Option: A survey
Kilian Hendrickx
Lorenzo Perini
Dries Van der Plas
Wannes Meert
Jesse Davis
MU
17
120
0
23 Jul 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
34
21
0
02 May 2021
Deep HyperNetwork-Based MIMO Detection
Deep HyperNetwork-Based MIMO Detection
Mathieu Goutay
Fayçal Ait Aoudia
J. Hoydis
18
53
0
07 Feb 2020
Bayesian Model-Agnostic Meta-Learning
Bayesian Model-Agnostic Meta-Learning
Taesup Kim
Jaesik Yoon
Ousmane Amadou Dia
Sungwoong Kim
Yoshua Bengio
Sungjin Ahn
UQCV
BDL
191
498
0
11 Jun 2018
Probabilistic Model-Agnostic Meta-Learning
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn
Kelvin Xu
Sergey Levine
BDL
165
666
0
07 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
243
11,659
0
09 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
132
3,263
0
09 Jun 2012
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