LCF- Guest Faculty Research Participant- Maria Pantoja
Argonne National Laboratory
Job posting number: #7127941 (Ref:415322)
Posted: March 13, 2023
Application Deadline: Open Until Filled
Training and validation of Neural Networks (NN) are very computationally intensive. Deep Learning (DL) methods have dominated image processing lately and are gaining relevance in visual simulations. Most DL models assume that the input data distribution is identical between test and validation, but most often, they are not. In other cases, the test data is uncertain, and labeling may differ based on individual expert evaluations. This discrepancy makes DL less reliable for tasks like traffic signal recognition for self-driving cars, medical images where labels are difficult to assign, and other tasks where the precision of the output is crucial. By adding the capability of propagating uncertainty to our results, image processing models can provide not just a single prediction of the identification but a distribution over predictions.
We would like to explore the differences in uncertainty evaluations using three different methods: Ensembles; Multiple Input Multiple Outputs; and Peer Loss Functions
Job FamilyVisiting Faculty Appointment
Job ProfileGuest Faculty Research Participant
Worker TypeShort-Term (Fixed Term)
Time TypeFull time
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