Abstract: Meta-learning, or learning to learn, offers a principled framework for few-shot learning. It leverages data from multiple related learning tasks ...
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In this work, we introduce the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators. In a.
This work introduces the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators ...
In this work, we introduce the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators. In a ...
Dive into the research topics of 'Learning to Learn to Demodulate with Uncertainty Quantification via Bayesian Meta-Learning'. Together they form a unique ...
Aug 2, 2021 · The capacity to quantify uncertainty in the model parameter space is further leveraged by extending Bayesian meta-learning to an active setting.
Bibliographic details on Learning to Learn to Demodulate with Uncertainty Quantification via Bayesian Meta-Learning.
Learning to learn to demodulate with uncertainty quantification via bayesian meta-learning. KM Cohen, S Park, O Simeone, S Shamai. WSA 2021; 25th ...
Learning to learn to demodulate with uncertainty quantification via bayesian meta-learning. KM Cohen, S Park, O Simeone, S Shamai. WSA 2021; 25th International ...
Sep 25, 2023 · Learning to Learn to Demodulate with Uncertainty Quantification via Bayesian Meta-Learning · Cohen, K. M., Park, S., Simeone, O. & Shamai, S ...