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Continual learning requires a model to adapt to ongoing changes in the data distribution, and often to the set of tasks to be performed. It is rare, however, that the data and task changes are completely unpredictable. Given a description of an overa
Externí odkaz:
http://arxiv.org/abs/2403.07356
Publikováno v:
37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), Dec 2023, New Orleans, United States
Continual learning (CL) aims to incrementally learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. Most CL works focus on tackling catastrophic forgetting under a learning-from-scratch paradigm.
Externí odkaz:
http://arxiv.org/abs/2307.02251
Autor:
McDonnell, Mark D., Jones, Eriita, Schwamb, Megan E., Aye, K-Michael, Portyankina, Ganna, Hansen, Candice J.
Dark deposits visible from orbit appear in the Martian south polar region during the springtime. These are thought to form from explosive jets of carbon dioxide gas breaking through the thawing seasonal ice cap, carrying dust and dirt which is then d
Externí odkaz:
http://arxiv.org/abs/2210.09152
Akademický článek
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Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such as classification and detection. However, BN layers introduce complexit
Externí odkaz:
http://arxiv.org/abs/1907.06916
Autor:
McDonnell, Mark C., Flynn, Raymond, Águila, Jesús Fernández, Hamill, Gerard A., Donohue, Shane, Benner, Eric M., Thomson, Christopher, Etsias, Georgios, Rowan, Thomas S.L., Wilkinson, Paul B., Meldrum, Philip I.
Publikováno v:
In Science of the Total Environment 1 April 2023 867