Category: AIR Seminar Series

“New Algorithms for Interpretable Machine Learning” Cynthia Rudin (Duke)

Cynthia Rudin, Duke University When: Monday, December 10, 2018 Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Where: Kilachand Center, 610 Commonwealth Ave. Boston, MA, Colloquium Room   Abstract: With the widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed models for medical imaging, and poor […]

“Building and Evaluating Conversational Agents”

João Sedoc, University of Pennsylvania Monday, December 3, 2018 11:00am – 12:00pm, refreshment & networking at 10:30am Hariri Institute for Computing 111 Cummington Mall Boston, MA 02215 Abstract: There has been a renewed focus on dialog systems, including non-task driven conversational agents (i.e. “chit-chat bots”). Dialog is a challenging problem since it spans multiple conversational turns. […]

Improving Face Verification Accuracy Using Hyperplane Similarity

This talk by Mike Jones, senior principal research scientist at Mitsubishi Electric Research Labs, will demonstrate how L2 distance is not the best basis of comparison to use in convolutional neural network (CNN) analysis for face verification and propose the hyperplane similarity as a more appropriate similarity function that is derived from the softmax loss function used to train the network.

Knowledge Transfer for Face Recognition

This talk by Zhengming Ding, a graduate student at Northeastern University, outlines a proposal to build a large-scale face recognizer capable of fighting off the data imbalance difficulty that existing machine learning approaches experience in mimicking human visual intelligence. To seek a more effective general classifier, we develop a novel generative model attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes.