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REPARTI

MIVIM

Titre
[1] Azadeh Mozafari, Hugo Siqueira Gomes, Wilson Leão and Christian Gagné,
"Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks",
in ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning, 6 2019.
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[2] Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille, Buyo Wang and Christian Gagné,
"A Principled Approach for Learning Task Similarity in Multitask Learning",
in International Joint Conference on Artificial Intelligence, 8 2019.
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[3] Sébastien De Blois, Ihsen Hedhli and Christian Gagné,
"Learning of Image Dehazing Models for Segmentation Tasks",
ArXiv e-prints, vol. 1903.01530, 03 2019.
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[4] Karol Lina Lopez, Christian Gagné and Marc-André Gardner,
"Demand-Side Management using Deep Learning for Smart Charging of Electric Vehicles",
IEEE Transactions on Smart Grid, vol. 10, no 3, 5 2019.
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[5] Azadeh Mozafari, Hugo Siqueira Gomes, Steeven Janny and Christian Gagné,
" A New Loss Function for Temperature Scaling to have Better Calibrated Deep Networks",
ArXiv e-prints, vol. 1810.11586, 10 2018.
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[6] Changjian Shui, Ihsen Hedhli and Christian Gagné,
" Accumulating Knowledge for Lifelong Online Learning",
ArXiv e-prints, vol. 1810.11479, 10 2018.
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[7] Audrey Durand, Theresa Wiesner, Marc-André Gardner, Louis-Émile Robitaille, Anthony Bilodeau, Christian Gagné, Paul De Koninck and Flavie Lavoie-Cardinal,
"A machine learning approach for automated optimization of super-resolution optical microscopy",
Nature Communications, vol. 9, no 5247, 12 2018.
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[8] Mahdieh Abbasi, Arezoo Rajabi, Azadeh Mozafari, Rakesh B. Bobba and Christian Gagné,
"Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection",
ArXiv e-prints, vol. 1808.08282, 8 2018.
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[9] Alejandro Cervantes, Christian Gagné, Pedro Isasi and Marc Parizeau,
"Evaluating and Characterizing Incremental Learning from Non-Stationary Data",
ArXiv e-prints, vol. 1806.06610, 6 2018.
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[10] Mahdieh Abbasi, Arezoo Rajabi, Christian Gagné and Rakesh B. Bobba,
"Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning",
in DSN Workshop on Dependable and Secure Machine Learning (DSML 2018), June 2018.
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