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of 21
pro vyhledávání: '"Bahaadini, Sara"'
In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models ca
Externí odkaz:
http://arxiv.org/abs/2205.13672
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task. The model benefits from pre-trained multilingual BERT. BERT hidden layers are shared among the two
Externí odkaz:
http://arxiv.org/abs/2011.02541
There has been a growing concern about the fairness of decision-making systems based on machine learning. The shortage of labeled data has been always a challenging problem facing machine learning based systems. In such scenarios, semi-supervised lea
Externí odkaz:
http://arxiv.org/abs/1912.13230
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 lab
Externí odkaz:
http://arxiv.org/abs/1811.04480
Autor:
Bahaadini, Sara, Noroozi, Vahid, Rohani, Neda, Coughlin, Scott, Zevin, Michael, Katsaggelos, Aggelos K.
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. The trained embedding function transfe
Externí odkaz:
http://arxiv.org/abs/1805.02296
Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples
Externí odkaz:
http://arxiv.org/abs/1706.03692
Autor:
Bahaadini, Sara, Rohani, Neda, Coughlin, Scott, Zevin, Michael, Kalogera, Vicky, Katsaggelos, Aggelos K
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to c
Externí odkaz:
http://arxiv.org/abs/1705.00034
Autor:
Zevin, Michael, Coughlin, Scott, Bahaadini, Sara, Besler, Emre, Rohani, Neda, Allen, Sarah, Cabero, Miriam, Crowston, Kevin, Katsaggelos, Aggelos K, Larson, Shane L, Lee, Tae Kyoung, Lintott, Chris, Littenberg, Tyson B, Lundgren, Andrew, Oesterlund, Carsten, Smith, Joshua R, Trouille, Laura, Kalogera, Vicky
Publikováno v:
Class. Quantum Grav. 34 (2017) 064003 (22pp)
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The e
Externí odkaz:
http://arxiv.org/abs/1611.04596
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