Discovering boundary conditions of attention state affecting object recognition

Autor: Blecourt, C.M. de, Maris, E.G.G., Peelen, M.V.

Publikováno v: Perception, 51, Suppl. 1, pp. 29-30
Perception, 51, 29-30

Item does not contain fulltext 2 p.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9116fedbc60c8a0b4aa8e12b1acce142
https://hdl.handle.net/2066/290052

Effect of field spread on resting-state MEG functional network analysis: A computational modeling study

Autor: Silva Pereira, S., Hindriks, R., Mühlberg, S., Maris, E.G.G., Ede, F.L. van, Griffa, A., Hagmann, P., Deco, G.

Publikováno v: Brain Connectivity, 7, 9, pp. 541-557
Brain Connectivity, 7, 541-557

Contains fulltext : 178654.pdf (Publisher’s version ) (Closed access) A popular way to analyze resting-state EEG and MEG data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel ti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::554f9ff7b1bf73f9f7c4be0d43905404
https://doi.org/10.1089/brain.2017.0525

Forward Amortized Inference for Likelihood-Free Variational Marginalization

Autor: Ambrogioni, L., Güçlü, U., Berezutskaya, Y., Borne, E.W.P. van den, Güçlütürk, Y., Hinne, M., Maris, E.G.G., Gerven, M.A.J. van, Chaudhuri, K., Sugiyama, M.

Publikováno v: Scopus-Elsevier
Chaudhuri, K.; Sugiyama, M. (ed.), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, 777-786. [S.l.] : [S.n.]
STARTPAGE=777;ENDPAGE=786;ISSN=2640-3498;TITLE=Chaudhuri, K.; Sugiyama, M. (ed.), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019
Chaudhuri, K.; Sugiyama, M. (ed.), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, pp. 777-786
CIÊNCIAVITAE

In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient ca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81af89b7371d9c4247d39551922584f1

Bayesian model ensembling using meta-trained recurrent neural networks

Autor: Ambrogioni, L., Berezutskaya, Y., Güçlü, U., Borne, E.W.P. van den, Güçlütürk, Y., Gerven, M.A.J. van, Maris, E.G.G.

Publikováno v: Workshop on Meta-Learning (MetaLearn 2017)-Neural Information Processing Systems (NIPS 2017), pp. 1-5
Workshop on Meta-Learning (MetaLearn 2017)-Neural Information Processing Systems (NIPS 2017), 1-5. [S.l.] : [S.n.]
STARTPAGE=1;ENDPAGE=5;TITLE=Workshop on Meta-Learning (MetaLearn 2017)-Neural Information Processing Systems (NIPS 2017)

Contains fulltext : 180405.pdf (Author’s version preprint ) (Open Access) In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optima

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3ffb0909213c7747754fca62ee90afe0
https://hdl.handle.net/2066/180405

Neuroimaging research data life-cycle management

Autor: Lee, H.C., Oostenveld, R., Boogert, H.J. van den, Maris, E.G.G.

Publikováno v: iRODS User Group Meeting 2017 Proceedings, 13-22. Chapel Hill, NC : iRODS Consortium
STARTPAGE=13;ENDPAGE=22;TITLE=iRODS User Group Meeting 2017 Proceedings
iRODS User Group Meeting 2017 Proceedings, pp. 13-22

Item does not contain fulltext Research Data Management (RDM) aims to improve the efficiency and transparency in the scientific process and to fullfil the requirements of the funding agencies and (local) regulations. Failures in reproducing some key

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::25ee504c60302484ba091416a1bf20bd
https://hdl.handle.net/2066/180894