20 citations · 36 across the 7 of their papers we have counts for
7 papers
PyRCA: A Library for Metric-based Root Cause Analysis
Chenghao Liu, Wenzhuo Yang, Himanshu Mittal +3
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framew…
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
Qian Cheng, Doyen Sahoo, Amrita Saha +6
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to…
LogAI: A Library for Log Analytics and Intelligence
Qian Cheng, Amrita Saha, Wenzhuo Yang +3
Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data t…
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum +11
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and…
Dynamic modeling of a sliding ring on an elastic rod with incremental potential formulation
Weicheng Huang, Peifei Xu, Zhaowei Liu
Mechanical interactions between rigid rings and flexible cables find broad application in both daily life (hanging clothes) and engineering systems (closing a tether-net). A reduce…
Nonlinear Dynamic Modeling of a Tether-net System for Space Debris Capture
Weicheng Huang, Dongze He, Yanbin Li +6
In this paper, a flexible tether-net system is applied to capture the space debris and a numerical framework is established to explore its nonlinear dynamic behaviors, which compri…
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation
Wenjing Yang, Ganghua Wang, Jie Ding +1
The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limite…