128 citations · 510 across the 23 of their papers we have counts for
4 papers · 1 filter
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…
Probabilistic neural networks for improved analyses with phenomenological models
C. H. Kim, K. Y. Chae, M. S. Smith +5
Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservab…
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput +7
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulat…
Noisy intermediate-scale quantum computers
Bin Cheng, Xiu-Hao Deng, Xiu Gu +18
Quantum computers have made extraordinary progress over the past decade, and significant milestones have been achieved along the path of pursuing universal fault-tolerant quantum c…