New Environment Adaptation with Few Shots for OFDM Receiver and mmWave
Beamforming

New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming

18 October 2023

Geoffrey Ye Li

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Papers citing "New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming"

6 / 6 papers shown

Title
BADM: Batch ADMM for Deep Learning Ouya Wang Shenglong Zhou Geoffrey Ye Li ODL 105 1 0 30 Jun 2024
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities Yisheng Song Ting-Yuan Wang S. Mondal J. P. Sahoo SLR 118 375 0 13 May 2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Colin Raffel Noam M. Shazeer Adam Roberts Katherine Lee Sharan Narang Michael Matena Yanqi Zhou Wei Li Peter J. Liu AIMat 450 20,298 0 23 Oct 2019
Meta-Transfer Learning for Few-Shot Learning Qianru Sun Yaoyao Liu Tat-Seng Chua Bernt Schiele 204 1,071 0 06 Dec 2018
Model-Driven Deep Learning for Physical Layer Communications Hengtao He Shi Jin Chao-Kai Wen Fei Gao Geoffrey Ye Li Zongben Xu AI4CE 60 375 0 17 Sep 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 825 11,937 0 09 Mar 2017