16 citations · 19 across the 6 of their papers we have counts for
6 papers
Coincident Learning for Beam-based RF Station Fault Identification Using Phase Information at the SLAC Linac Coherent Light Source
Jia Liang, William Colocho, Franz-Josef Decker +7
Anomalies in radio-frequency (RF) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC's Linac Coherent Light Source (LCLS). De…
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Ryan Humble, William Colocho, Finn O'Shea +2
Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (…
Coincident Learning for Unsupervised Anomaly Detection
Ryan Humble, Zhe Zhang, Finn O'Shea +2
Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to…
Soft Masking for Cost-Constrained Channel Pruning
Ryan Humble, Maying Shen, Jorge Albericio Latorre +2
Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of netw…
Beam-based RF Station Fault Identification at the SLAC Linac Coherent Light Source
Ryan Humble, Finn H. O'Shea, William Colocho +4
Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with…
Second Order Accurate Hierarchical Approximate Factorization of Sparse SPD Matrices
Bazyli Klockiewicz, Léopold Cambier, Ryan Humble +2
We describe a second-order accurate approach to sparsifying the off-diagonal blocks in the hierarchical approximate factorizations of sparse symmetric positive definite matrices. T…