The University of Tennessee Health Science Center

11/04/2025 | News release | Distributed by Public on 11/04/2025 09:33

Department of Preventive Medicine Biostatistics Seminar Series: Combining Symbolic Relational Knowledge with Neural Representations

The Division of Biostatistics of the Department of Preventive Medicine, UTHSC, invites you to attend the following seminar.

Time: Monday, November 10, 2025, 2:00 - 3:00 pm CT

Venue: Doctor's Office Building, 66 N Pauline Street, Conference Room 400

ZOOM Virtual Room Connection: Register in advance for this meeting to get the Zoom Link

Seminar Website: https://www.uthsc.edu/preventive-medicine/events.php

Speaker Bio: https://sites.google.com/site/dvngopal/home

Combining Symbolic Relational Knowledge with Neural Representations

Deepak Venugopal, PhD

Department of Computer Science, The University of Memphis

Combining symbolic representations with neural representations is a topic that has obtained significant attention to improve interpretability, scalability and generalization in deep learning models. Kautz recently proposed a taxonomy of different possible Neuro-Symbolic integrations. In this talk, first, I will present an overview of such integrations and then summarize recent research results from our group in this direction. Specifically, I will present a model called Hybrid Markov Logic Networks (HMLNs) to combine embeddings learned from a Deep Neural Network (DNN) with symbolic relational knowledge that can express domain constraints. In particular, for this work, we developed approaches to address covariate shifts in neural embeddings that occur due to variability in deep learning. Finally, I will present an overview of applications in education that we are developing in collaboration with Carnegie Learning using symbolic knowledge in conjunction with DNNs.

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The University of Tennessee Health Science Center published this content on November 04, 2025, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on November 04, 2025 at 15:33 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]