Search Results for author: So Hasegawa

Found 2 papers, 0 papers with code

Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve

no code implementations7 Dec 2022 Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Grosse

Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications.

A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN

no code implementations3 Aug 2022 Aohan Li, Ikumi Urabe, Minoru Fujisawa, So Hasegawa, Hiroyuki Yasuda, Song-Ju Kim, Mikio Hasegawa

(1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels.

Fairness reinforcement-learning +1

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