Rule extraction from deep neural nets

Add to your list(s) Download to your calendar using vCal

• Urska Matjasec (University of Ljubljana)
• Friday 08 November 2019, 16:00-17:00
• FC22, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Mateja Jamnik.

Deep neural nets are a black box approach to statistical learning. The classification from the model is hard to interpret. We present an approach to extracting rules from a deep learning model that gets cancer patients’ gene expression as input and classifies them as those likely or unlikely to relapse. In this way we get some level of explainability of the output from the deep learning model.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Artificial Intelligence Research Group Talks (Computer Laboratory)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Department of Computer Science and Technology talks and seminars
• FC22, Computer Laboratory, William Gates Building
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• School of Technology
• Speech Seminars
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.