Category: AIR Seminar Series

“‘Does This Vehicle Belong to You?’ Processing the Language of Policing for Improving Police-Community Relations” Dan Jurafsky (Stanford)

Dan Jurafsky, Stanford University Monday, October 22, 2018, 10:30am Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Kilachand Center, 610 Commonwealth Avenue Boston, MA 02215   “Does This Vehicle Belong to You?” Processing the Language of Policing for Improving Police-Community Relations Abstract: Police body-worn cameras have the potential to play an important role in understanding and improving police-community relations. […]

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.

Medical Image Analysis Projects at Uppsala University

Filip Malmberg, an associate professor with the Centre for Image Analysis at Sweden’s Uppsala University, provides an overview of ongoing research projects in medical image analysis at Uppsala University that offer a broad range of interesting research challenges, from solving large scale combinatorial optimization problems to developing efficient interfaces for complex 3D user interaction.