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of 87
pro vyhledávání: '"Hannagan, Thomas"'
Autor:
Wang, Yixiao, Tang, Chen, Sun, Lingfeng, Rossi, Simone, Xie, Yichen, Peng, Chensheng, Hannagan, Thomas, Sabatini, Stefano, Poerio, Nicola, Tomizuka, Masayoshi, Zhan, Wei
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high computational demands. To tackle these challenges, we introduce Optimal
Externí odkaz:
http://arxiv.org/abs/2408.00766
Domain adaptive active learning is leading the charge in label-efficient training of neural networks. For semantic segmentation, state-of-the-art models jointly use two criteria of uncertainty and diversity to select training labels, combined with a
Externí odkaz:
http://arxiv.org/abs/2311.14146
The elusive nature of gradient-based optimization in neural networks is tied to their loss landscape geometry, which is poorly understood. However recent work has brought solid evidence that there is essentially no loss barrier between the local solu
Externí odkaz:
http://arxiv.org/abs/2310.10171
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Bertrand, David, Hannagan, Thomas
Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large netwo
Externí odkaz:
http://arxiv.org/abs/2206.06255
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Bertrand, David, Hannagan, Thomas
Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks. However, depending on the architecture, pruning introduces dimensional discrepancies which preven
Externí odkaz:
http://arxiv.org/abs/2206.06247
Autor:
Tessier, Hugo, Gripon, Vincent, Léonardon, Mathieu, Arzel, Matthieu, Hannagan, Thomas, Bertrand, David
Publikováno v:
Journal of Imaging 8 (2022), no. 3: 64
Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning approaches still face core issues that hinder their performance or sca
Externí odkaz:
http://arxiv.org/abs/2011.10520
Autor:
Hannagan, Thomas, Amedi, Amir, Cohen, Laurent, Dehaene-Lambertz, Ghislaine, Dehaene, Stanislas
Publikováno v:
In Trends in Cognitive Sciences July 2015 19(7):374-382
Autor:
Ziegler, Johannes C., Dufau, Stéphane, Montant, Marie, Hannagan, Thomas, Fagot, Joël, Grainger, Jonathan
Publikováno v:
Psychological Science, 2013 Sep 01. 24(9), 1870-1871.
Externí odkaz:
https://www.jstor.org/stable/23484692
Autor:
Ziegler, Johannes C., Hannagan, Thomas, Dufau, Stéphane, Montant, Marie, Fagot, Joël, Grainger, Jonathan
Publikováno v:
Psychological Science, 2013 Aug 01. 24(8), 1609-1611.
Externí odkaz:
http://dx.doi.org/10.1177/0956797612474322
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