33 citations · 64 across the 9 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
eess.IV2021
Insights from Generative Modeling for Neural Video Compression
Ruihan Yang, Yibo Yang, Joseph Marino +1
While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this wor…
stat.ML2021
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché, Valentin Thomas, Rafael Pardinas +4
Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-ch…