University of Cambridge > Talks.cam > Language Technology Lab Seminars > Interpretability as the Inverse Machine Learning Pipeline

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Interpretability as the Inverse Machine Learning Pipeline

Download to your calendar using vCal

If you have a question about this talk, please contact Lucas Resck .

Bio: Prof. Sarah Wiegreffe is an natural language processing and machine learning researcher and an assistant professor in the Department of Computer Science at the University of Maryland. She works on the explainability and interpretability of deep learning systems for language, with a focus on understanding how language models make predictions in order to make them more reliable, safe, and transparent to human users. She has been honored as a 3-time Rising Star in EECS , Machine Learning, and Generative AI. She was previously a postdoc at the Allen Institute for AI and the University of Washington and, before that, received her Ph.D. and M.S. degrees from Georgia Tech.

This talk is part of the Language Technology Lab Seminars series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Lightfoot Room, St John's College, Divinity School, CB2 1TP Centre for Mathematical Sciences, MR11 Part II Lecture Theatre, Department of Genetics, Downing Site

Other talks

Seminars in Cancer Online Causal Inference Seminar Using numerical simulations for solving spectral geometry problems Teams Announced Tea and Coffee Break Title TBC
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity