Abstract: Transfer learning refers to the process of transferring the learning from one task to another related task. Transfer learning is especially beneficial in situations where we have limited ...

The proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability ...

MobiCollab enables proximity-based collaborative transfer learning where nearby mobile devices discover each other, share training data, and collectively improve an on-device image classification ...

Nearly four in 10 adult Americans have tried to transfer credit toward a college degree or credential. Of those, 58 percent lost credits in the process. For some, the consequences were severe: using ...

Abstract: Overfitting, an issue that constrains the validity and generalizability of machine learning in neuroimaging-based diagnostic-classification, is in part due to small sample sizes in relation ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Garching 85748, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Biophotonic technologies such as Raman spectroscopy are powerful tools for obtaining ...