Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Quentin Jodelet"'
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks i
Externí odkaz:
https://doaj.org/article/e652145894a44d6f8780c17e45b85b2a
Autor:
Marc Pickett, Zheda Mai, German Ignacio Parisi, Davide Maltoni, Quentin Jodelet, Massimo Caccia, Nikhil Churamani, Qi She, Lorenzo Pellegrini, Yu Chen, Issam H. Laradji, Vincenzo Lomonaco, Pau Rodríguez, Ruiping Wang, David Vazquez
Publikováno v:
Artificial Intelligence. 303:103635
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the signific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38796e4705b92946c33032bd6155c5b4
https://doi.org/10.1016/j.artint.2021.103635
https://doi.org/10.1016/j.artint.2021.103635
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation ISBN: 9783030304867
ICANN (1)
Theoretical Neural Computation
Theoretical Neural Computation, pp.497-512, 2019, ⟨10.1007/978-3-030-30487-4_39⟩
ICANN (1)
Theoretical Neural Computation
Theoretical Neural Computation, pp.497-512, 2019, ⟨10.1007/978-3-030-30487-4_39⟩
In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design deep neural networks which support incremental lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03c01a6b41223e93ab6a5cd6dd097479
https://doi.org/10.1007/978-3-030-30487-4_39
https://doi.org/10.1007/978-3-030-30487-4_39
