Oxford Events, the new replacement for OxTalks, will launch on 16th March. The two-week OxTalks freeze period starts on Monday 2nd March. During this time, there will be no facility to publish or edit events. The existing OxTalks site will remain available to view during this period. Once Oxford Events launches, you will need a Halo login to submit events. Full details are available on the Staff Gateway.
Big, multidimensional data such as brain/heart MRI is expected to transform healthcare. However, such data poses great challenges, particularly the need for interpretation and very high dimensionality accompanied by a relatively small sample size. Deep learning models are powerful but inadequate to address these problems, due to their opaque and data-hungry nature. This talk will present tensor-based machine learning models for extracting/selecting compact, interpretable features directly from tensor representations of multidimensional data. I will show their applications in prediction and interpretation of brain fMRI for neural decoding and cardiac MRI for disease diagnosis. Finally, I will discuss some ongoing and future research works on interpretable machine learning, transfer learning, and network embedding.
