AIML Seminar: "Explainable Graphical Models"

Wednesday, November 20, 2019 at 4:00pm to 5:00pm

Building C, 115
113 Research Dr, Bethlehem, PA 18015

Presentation by Sihong Xie, CSE.
Abstract: Structured predictive models based on (neural) graphical models and optimization have state-of-the-art performance in machine learning applications, such as NLP, computational neural science, and fraud detection. However, these models are complex, can be attacked, or trained on noisy data. I am working on the debugging and explanation for structured predictive models, so that the models are transparent, secured, and robust. The methodologies are based on search (in the computer science sense), reinforcement learning, and sensitivity analysis.



Academics & Colleges

Target Audience

Graduate Students, Faculty

Computer Science and Engineering Department, P.C. Rossin College of Engineering and Applied Science, Institute for Data, Intelligent Systems & Computation
Contact Information

Sarah Wing

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