Data-efficient and explainable machine learning
Lead Research Organisation:
University of Surrey
Department Name: Vision Speech and Signal Proc CVSSP
Abstract
This research will investigate novel ML approaches (particularly NN based) that deliver improved performance, robustness, and interpretability particularly for problems where data is limited and noisy (contaminated). The focus will be on new neural architectures, data scheduling and pre-processing, novel training objectives and matching optimisation methods. Architectures that
Organisations
People |
ORCID iD |
Miroslaw Bober (Primary Supervisor) | |
Adam Dowse (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/T518050/1 | 30/09/2020 | 29/09/2025 | |||
2644086 | Studentship | EP/T518050/1 | 03/02/2022 | 30/12/2024 | Adam Dowse |