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

no code implementations • 7 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 implementations • 3 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