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Category:
Natural Sciences

Lecturer:
Prof. Bin Yu, Departments of Statistics and EECS, University of California, Berkeley, USA

Place:
HITS, Carl-Bosch-Auditorium of Studio Villa Bosch, Schloss-Wolfsbrunnenweg 33

Host:
Heidelberg Institute for Theorectical Studies (HITS)

Description:
In this talk, I’d like to discuss the intertwining importance and connections of three principles of data science in the title.They will be demonstrated in the context of two collaborative projects in neuroscience and genomics, respectively. The first project in neuroscience uses transfer learning to integrate fitted convolutional neural networks (CNNs) on ImageNet with regression methods to provide predictive and stable characterizations of neurons from the challenging primary visual cortex V4. The second project proposes iterative random forests (iRF) as a stabilized RF to seek predictable and interpretable high-order interactions between biomolecules.

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