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Kaiyu Suzuki and Tomofumi Matsuzawa
Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fi...
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Rashid Mustafa, Pijush Samui and Sunita Kumari
Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic app...
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Amedeo Buonanno, Antonio Nogarotto, Giuseppe Cacace, Giovanni Di Gennaro, Francesco A. N. Palmieri, Maria Valenti and Giorgio Graditi
In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are...
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Sima Rastayesh, Amol Mankar, John Dalsgaard Sørensen and Sajjad Bahrebar
This paper presents recent contributions to the Marie Sklodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability...
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Austin D. Lewis and Katrina M. Groth
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current sys...
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