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cs.LG2024
Dynamic Importance Learning using Fisher Information Matrix (FIM) for Nonlinear Dynamic Mapping
Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shoorehdeli
Understanding output variance is critical in modeling nonlinear dynamic systems, as it reflects the system's sensitivity to input variations and feature interactions. This work pre…
cs.LG2023
Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification
Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shooredeli
To benefit from the modeling capacity of deep models in system identification, without worrying about inference time, this study presents a novel training strategy that uses deep m…
cs.LG2023
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis
Ardavan Modarres, Vahid Mohammad-Zadeh Eivaghi, Mahdi Aliyari Shoorehdeli +1
Due to the incapability of one sensory measurement to provide enough information for condition monitoring of some complex engineered industrial mechanisms and also for overcoming t…