activity
20172021
most citedEstimating Fault Friction from Seismic Signals in the Laboratory

107 citations · 254 across the 11 of their papers we have counts for

collaborators

12 papers

cs.LG2021★ 1 cited

Learning Continuous Exponential Families Beyond Gaussian

Christopher X. Ren, Sidhant Misra, Marc Vuffray +1

We address the problem of learning of continuous exponential family distributions with unbounded support. While a lot of progress has been made on learning of Gaussian graphical mo…

physics.geo-ph2020

Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation

Claudia Hulbert, Romain Jolivet, Blandine Gardonio +3

Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often ass…

eess.IV2020

Deep Snow: Synthesizing Remote Sensing Imagery with Generative Adversarial Nets

Christopher X. Ren, Amanda Ziemann, James Theiler +1

In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training…

stat.AP2020★ 2 cited

On the Detectability of Conflict: a Remote Sensing Study of the Rohingya Conflict

Christopher X. Ren, Matthew T. Calef, Alice M. S. Durieux +2

The detection and quantification of conflict through remote sensing modalities represents a challenging but crucial aspect of human rights monitoring. In this work we demonstrate h…

stat.AP2020

BUDD: Multi-modal Bayesian Updating Deforestation Detections

Alice M. S Durieux, Christopher X. Ren, Matthew T. Calef +2

The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updati…

cs.CV2019

Feature Augmentation Improves Anomalous Change Detection for Human Activity Identification in Synthetic Aperture Radar Imagery

Hannah J. Murphy, Christopher X. Ren, Matthew T. Calef

Anomalous change detection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In thi…