Whether estimating the probability that a disease is present or forecasting risk of deterioration,1 readmission,2 or death,3 most contemporary clinical artificial intelligence (AI) systems are ...
Objective We employed a causal inference framework to estimate the counterfactual dose-response effects of light-intensity physical activity (LPA) on mortality across low, medium and high moderate- to ...
OpenVINO provides powerful Python APIs for model conversion and inference, as well as OpenVINO Model Server (OVMS) for production deployments. However, there is currently no official lightweight REST ...
Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...
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 ...
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 ...
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 ...
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