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of 25
pro vyhledávání: '"Jane X. Wang"'
Autor:
Andrea Lancichinetti, M. Irmak Sirer, Jane X. Wang, Daniel Acuna, Konrad Körding, Luís A. Nunes Amaral
Publikováno v:
Physical Review X, Vol 5, Iss 1, p 011007 (2015)
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, stat
Externí odkaz:
https://doaj.org/article/933c04ee55c34c3ebd8d2973dced6a3f
Autor:
Patrick Watson, Joseph L. Holtrop, Hillary Schwarb, Matthew D. J. McGarry, Joel L. Voss, Curtis L. Johnson, Neal J. Cohen, Jane X. Wang, Bradley P. Sutton, Michael R. Dulas
Publikováno v:
Journal of Cognitive Neuroscience. 31:1857-1872
Declarative memory is supported by distributed brain networks in which the medial-temporal lobes (MTLs) and pFC serve as important hubs. Identifying the unique and shared contributions of these regions to successful memory performance is an active ar
Autor:
Demis Hassabis, Samuel Ritter, Zeb Kurth-Nelson, Charles Blundell, Matthew Botvinick, Jane X. Wang
Publikováno v:
Trends in Cognitive Sciences. 23:408-422
Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive
Publikováno v:
PLoS ONE, Vol 8, Iss 3, p e59204 (2013)
Reading requires the interaction of a distributed set of cortical areas whose distinct patterns give rise to a wide range of individual skill. However, the nature of these neural interactions and their relation to reading performance are still poorly
Externí odkaz:
https://doaj.org/article/161d1b1d699d45abae9023ac1dad6ead
The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. Howeve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81537c02e6b742aefaf6df5cab8f6dc3
http://arxiv.org/abs/2007.03750
http://arxiv.org/abs/2007.03750
Autor:
Jane X. Wang
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is cu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad384d6056ada01e508b34112ec53120
Recent research has placed episodic reinforcement learning (RL) alongside model-free and model-based RL on the list of processes centrally involved in human reward-based learning. In the present work, we extend the unified account of model-free and m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b5fd1a47f84bc0a441e3b2013e3ddb7
Autor:
Dhruva Tirumala, Dharshan Kumaran, Matthew Botvinick, Zeb Kurth-Nelson, Demis Hassabis, Hubert Soyer, Jane X. Wang, Joel Z. Leibo
Publikováno v:
Nature Neuroscience
Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine ‘stamps in’ associations between situations, actions and rewards by modulating the strength o
Publikováno v:
Hippocampus. 25:1028-1041
Although hippocampus unequivocally supports explicit/declarative memory, fewer findings have demonstrated its role in implicit expressions of memory. We tested for hippocampal contributions to an implicit expression of configural/relational memory fo
Autor:
Joel L. Voss, Jane X. Wang
Publikováno v:
Hippocampus. 25:877-883
Noninvasive stimulation can alter the function of brain networks, although the duration of neuroplastic changes are uncertain and likely vary for different networks and stimulation parameters. We have previously shown that multiple-day repetitive tra