1 code implementation • 26 Sep 2020 • Hossein Sharifi-Noghabi, Hossein Asghari, Nazanin Mehrasa, Martin Ester
To learn a domain-invariant representation, it also utilizes a novel alignment loss to ensure that the distance between pairs of class centroids, computed after adding the unlabeled samples, is preserved across different domains.
no code implementations • 18 Oct 2019 • Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, JiaWei He, Thibaut Durand, Marcus Brubaker, Greg Mori
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature.
no code implementations • pproximateinference AABI Symposium 2019 • Micael Carvalho, Thibaut Durand, JiaWei He, Nazanin Mehrasa, Greg Mori
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.
no code implementations • CVPR 2019 • Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, JiaWei He, Leonid Sigal, Greg Mori
We propose a novel probabilistic generative model for action sequences.
no code implementations • CVPR 2019 • Thibaut Durand, Nazanin Mehrasa, Greg Mori
Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex.
no code implementations • 3 Jun 2017 • Nazanin Mehrasa, Yatao Zhong, Frederick Tung, Luke Bornn, Greg Mori
Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics.