Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...
Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...
What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...
ABSTRACT: In recent years, with its powerful enabling effect, data factor has become a crucial engine for generating and fostering new quality productive forces. Based on constructing a theoretical ...
In many enterprise environments, engineers and technical staff need to find information quickly. They search internal documents such as hardware specifications, project manuals, and technical notes.
Cybersecurity researchers have uncovered critical remote code execution vulnerabilities impacting major artificial intelligence (AI) inference engines, including those from Meta, Nvidia, Microsoft, ...
Animals excel at seamlessly integrating information from different senses, a capability critical for navigating complex environments. Despite recent progress in multisensory research, the absence of ...
Background: Traditional congenital heart surgery quality assessments rely on indirect standardization via regression, which can be complicated by heterogeneity in case-mix, surgical volume, and low ...
Please join the Department of Epidemiology Center for Clinical Trials and Evidence Synthesis (CCTES) and Center for Drug Safety and Effectiveness (CDSE) in welcoming Elizabeth Stuart, PhD, AM, Chair ...
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