166 citations · 507 across the 29 of their papers we have counts for
4 papers · 1 filter
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Yibo Yang, Stephan Mandt
Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterio…
Supervised Compression for Resource-Constrained Edge Computing Systems
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural netwo…
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…
Learning to Simulate High Energy Particle Collisions from Unlabeled Data
Jessica N. Howard, Stephan Mandt, Daniel Whiteson +1
In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model pre…