1 code implementation • COLING (MWE) 2020 • Shiva Taslimipoor, Sara Bahaadini, Ekaterina Kochmar
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task.
no code implementations • 31 Dec 2019 • Vahid Noroozi, Sara Bahaadini, Samira Sheikhi, Nooshin Mojab, Philip S. Yu
There has been a growing concern about the fairness of decision-making systems based on machine learning.
no code implementations • 11 Nov 2018 • Vahid Noroozi, Sara Bahaadini, Lei Zheng, Sihong Xie, Weixiang Shao, Philip S. Yu
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.
Dimensionality Reduction Learning Representation Of Multi-View Data
no code implementations • 7 May 2018 • Sara Bahaadini, Vahid Noroozi, Neda Rohani, Scott Coughlin, Michael Zevin, Aggelos K. Katsaggelos
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
no code implementations • 12 Jun 2017 • Vahid Noroozi, Lei Zheng, Sara Bahaadini, Sihong Xie, Philip S. Yu
The model consists of two complementary components.
no code implementations • 28 Apr 2017 • Sara Bahaadini, Neda Rohani, Scott Coughlin, Michael Zevin, Vicky Kalogera, Aggelos K. Katsaggelos
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.