no code implementations • MSR (COLING) 2020 • Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens
In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar.
no code implementations • 6 Apr 2023 • Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak, Alexandre See, Samira Ebrahimi Kahou, Thomas Fevens, Mohammad Havaei
In addition, PD is augmented with a weighted MI maximization objective for label distribution shift.
1 code implementation • 31 Dec 2021 • Laya Rafiee Sevyeri, Thomas Fevens
Identifying anomalies refers to detecting samples that do not resemble the training data distribution.
no code implementations • 26 Jan 2021 • Prabhakara Subramanya Jois, Aniketh Manjunath, Thomas Fevens
With the emergence and advancements of deep learning for digital healthcare, several methodologies have been proposed for such segmentation tasks.
1 code implementation • 9 Mar 2020 • Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei, Francis Dutil, Thomas Fevens, Mohammad Havaei
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
no code implementations • WS 2019 • Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens
This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.
1 code implementation • 18 Oct 2019 • Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, Joseph Paul Cohen
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.
no code implementations • ICCV 2019 • Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader, Francis Dutil, Lisa Di Jorio, Thomas Fevens
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
no code implementations • 28 May 2019 • Qicheng Lao, Thomas Fevens
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined.
no code implementations • 26 Jun 2018 • Qicheng Lao, Thomas Fevens, Boyu Wang
Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning.