51 citations · 64 across the 8 of their papers we have counts for
8 papers
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati +3
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model paramet…
Bidirectional Linear Recurrent Models for Sequence-Level Multisource Fusion
Qisai Liu, Zhanhong Jiang, Joshua R. Waite +3
Sequence modeling is a critical yet challenging task with wide-ranging applications, especially in time series forecasting for domains like weather prediction, temperature monitori…
Asynchronous Training Schemes in Distributed Learning with Time Delay
Haoxiang Wang, Zhanhong Jiang, Chao Liu +3
In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance. This issue is usually tackled by delay tolerant a…
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems
Chao Liu, Kin Gwn Lore, Zhanhong Jiang +1
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, di…
Predicting County Level Corn Yields Using Deep Long Short Term Memory Models
Zehui Jiang, Chao Liu, Nathan P. Hendricks +3
Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can hel…
Energy Prediction using Spatiotemporal Pattern Networks
Zhanhong Jiang, Chao Liu, Adedotun Akintayo +2
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dy…
Data-driven root-cause analysis for distributed system anomalies
Chao Liu, Kin Gwn Lore, Soumik Sarkar
Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong co…
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS
Chao Liu, Sambuddha Ghosal, Zhanhong Jiang +1
Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault propa…