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 […]
Category: AIR Seminar Series
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. […]
Aishwarya Agrawal, Georgia Tech Wednesday, October 31, 2018 11:30am – 12:30pm in MCS 148 Hariri Institute for Computing 111 Cummington Mall Boston, MA 02215 Abstract: In this talk, I will present our work on Visual Question Answering (VQA) — I will provide a brief overview of the VQA task, dataset and baseline models, as well as, […]
BU postdoctoral associate Bryan Plummer introduces an approach to improving many image-language tasks where neural networks learn a set of models, each of which capture a different concept which is useful in the task.
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.
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.
This talk by Andrei Barbu, a research scientist at MIT, will discuss a program to unify research around a number of vision-language problems into a single mathematical framework culminating in a robotic platform that is able to follow natural language commands, store knowledge, and answer questions.
This talk by Guorong Li, an associate professor at the University of Chinese Academy of Sciences, will outline recent research in media analysis including learning label-specific features for multi-label classification, learning common space for cross-modal retrieval and car tracking in UAV video.
Craig Yu, an associate professor at UMass Boston, discusses recent progress in data-driven computational design, including automatic interior design, architectural design, zoomorphic furniture design, and the design of reconfigurable objects and dissections puzzles.
Mélina Skouras, a postdoctoral associate at MIT, presents novel algorithms based on physics-based simulation and inverse modeling that can be used to alleviate difficulties in digital manufacturing and allow the designer to easily create custom deformable objects.