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Number of items: 10.
B
Bang, D;
Fusaroli, R;
Tylén, K;
Olsen, K;
Latham, PE;
Lau, JY;
Roepstorff, A;
... Bahrami, B; + view all
(2014)
Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making.
Conscious Cogn
, 26C
13 - 23.
10.1016/j.concog.2014.02.002.
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Gonçalves, PJ;
Arrenberg, AB;
Hablitzel, B;
Baier, H;
Machens, CK;
(2014)
Optogenetic perturbations reveal the dynamics of an oculomotor integrator.
Frontiers in Neural Circuits
, 8
, Article 10. 10.3389/fncir.2014.00010.
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Grabska-Barwińska, A;
Latham, PE;
(2014)
How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
Journal of Computational Neuroscience
, 36
(3)
pp. 469-481.
10.1007/s10827-013-0481-5.
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P
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Park, M;
Jitkrittum, W;
Qamar, A;
Szabo, Z;
Buesing, L;
Sahani, M;
(2014)
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM).
In:
Advances in Neural Information Processing Systems 28 (NIPS 2015).
Neural Information Processing Systems Foundation: Montreal, Canada.
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Szabo, Z;
(2014)
Information Theoretical Estimators Toolbox.
Journal of Machine Learning Research
, 15
283 - 287.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Consistent Distribution Regression via Mean Embedding.
Presented at: University of Hertfordshire, Computer Science Research Colloquium, Hatfield, UK.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Distribution Regression - the Set Kernel Heuristic is Consistent.
Presented at: CSML Lunch Talk Series, London, UK.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Learning on Distributions.
Presented at: Kernel methods for big data workshop, Lille, France.
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Szabo, Z;
Gretton, A;
Póczos, B;
Sriperumbudur, B;
(2014)
Simple consistent distribution regression on compact metric domains.
Presented at: UCL-Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD-2014), London, UK.
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Szabo, Z;
Gretton, A;
Poczos, B;
Sriperumbudur, B;
(2014)
Vector-valued distribution regression: a simple and consistent approach.
Presented at: Statistical Science Seminars, London, UK.
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