Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Razvan Pascanu"'
Publikováno v:
Neural Information Processing ISBN: 9783031301049
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::102b5bd0bb744d1aecb96b8d5917f0d7
https://doi.org/10.1007/978-3-031-30105-6_49
https://doi.org/10.1007/978-3-031-30105-6_49
Publikováno v:
Trends in cognitive sciences. 24(12)
Artificial intelligence research has seen enormous progress over the past few decades, but it predominantly relies on fixed datasets and stationary environments. Continual learning is an increasingly relevant area of study that asks how artificial sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69a11846e21c0dd354020727b672927d
https://pubmed.ncbi.nlm.nih.gov/33158755
https://pubmed.ncbi.nlm.nih.gov/33158755
Autor:
Demis Hassabis, Raia Hadsell, Stephen Gaffney, Ross Goroshin, Timothy P. Lillicrap, Charles Beattie, Helen King, Caswell Barry, Piotr Mirowski, Neil C. Rabinowitz, Charles Blundell, Benigno Uria, Andrea Banino, Koray Kavukcuoglu, Joseph Modayil, Alexander Pritzel, Amir Sadik, Thomas Degris, Greg Wayne, Dharshan Kumaran, Stig Petersen, Brian Hu Zhang, Fabio Viola, Martin J. Chadwick, Razvan Pascanu, Hubert Soyer
Publikováno v:
Nature. 557:429-433
Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19b8abdc695cb8351ef270f29574661c
https://doi.org/10.1038/s41586-018-0102-6
https://doi.org/10.1038/s41586-018-0102-6
Publikováno v:
University of Manchester-PURE
Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural net
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d9fa927a04437f0ada897da74c76285
http://arxiv.org/abs/1909.00025
http://arxiv.org/abs/1909.00025
Autor:
Joel Veness, Demis Hassabis, Agnieszka Grabska-Barwinska, John Quan, Dharshan Kumaran, Claudia Clopath, James Kirkpatrick, Andrei Rusu, Guillaume Desjardins, Neil C. Rabinowitz, Razvan Pascanu, Raia Hadsell, Kieran Milan, Tiago Ramalho
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America. 115(11)
In our recent work on elastic weight consolidation (EWC) (1) we show that forgetting in neural networks can be alleviated by using a quadratic penalty whose derivation was inspired by Bayesian evidence accumulation. In his letter (2), Dr. Huszar prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cd7bed6a7820cf9cbd4da56b8658b5c
https://pubmed.ncbi.nlm.nih.gov/29463735
https://pubmed.ncbi.nlm.nih.gov/29463735
Autor:
Guillaume Desjardins, Agnieszka Grabska-Barwinska, James Kirkpatrick, Demis Hassabis, Razvan Pascanu, Raia Hadsell, Andrei Rusu, Claudia Clopath, Neil C. Rabinowitz, Joel Veness, John Quan, Dharshan Kumaran, Tiago Ramalho, Kieran Milan
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6be28317d1dd41f3621728ed5132ecf3
https://europepmc.org/articles/PMC5380101/
https://europepmc.org/articles/PMC5380101/
Autor:
Luca Maria Aiello, Douglas Eck, Michael I. Mandel, Yoshua Bengio, Razvan Pascanu, Filippo Menczer, Rossano Schifanella
Publikováno v:
ACM Transactions on Multimedia Computing, Communications, and Applications. :1-18
This article examines the use of two kinds of context to improve the results of content-based music taggers: the relationships between tags and between the clips of songs that are tagged. We show that users agree more on tags applied to clips tempora
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad5a3fbbf029de24d52b22ed6a2f08ce
https://doi.org/10.1145/2037676.2037689
https://doi.org/10.1145/2037676.2037689
Autor:
Razvan Pascanu, Herbert Jaeger
Publikováno v:
Neural Networks. 24:199-207
Neurodynamical models of working memory (WM) should provide mechanisms for storing, maintaining, retrieving, and deleting information. Many models address only a subset of these aspects. Here we present a rather simple WM model in which all of these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf36a9a5767477822cacdeb93e3e6b15
https://doi.org/10.1016/j.neunet.2010.10.003
https://doi.org/10.1016/j.neunet.2010.10.003
Publikováno v:
ICASSP
Attackers often create systems that automatically rewrite and reorder their malware to avoid detection. Typical machine learning approaches, which learn a classifier based on a handcrafted feature vector, are not sufficiently robust to such reorderin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::776b3e97aeebb65cba2ce8932722ae2c
https://doi.org/10.1109/icassp.2015.7178304
https://doi.org/10.1109/icassp.2015.7178304
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783662448472
ECML/PKDD (1)
ECML/PKDD (1)
In this paper we propose and investigate a novel nonlinear unit, called L p unit, for deep neural networks. The proposed L p unit receives signals from several projections of a subset of units in the layer below and computes a normalized L p norm. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef37e9c8207016f3a69e982da8d3e9f2
https://doi.org/10.1007/978-3-662-44848-9_34
https://doi.org/10.1007/978-3-662-44848-9_34