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Probabilistic models with deep neural networks

Masegosa, Andrés R. ; Cabañas, Rafael ; Langseth, Helge ; Nielsen, Thomas D. ; Salmerón, Antonio

In: Entropy, 2021, vol. 23, no. 1, p. 27 p

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...

Dictionary Learning for Fast Classification Based on Soft-thresholding

Fawzi, Alhussein ; Davies, Mike ; Frossard, Pascal

In: International Journal of Computer Vision, 2015, vol. 114, no. 2-3, p. 306-321

Neural networks for regional employment forecasts: are the parameters relevant?

Patuelli, Roberto ; Reggiani, Aura ; Nijkamp, Peter ; Schanne, Norbert

In: Journal of Geographical Systems, 2011, vol. 13, no. 1, p. 67-85

Neuroevolution: from architectures to learning

Floreano, Dario ; Dürr, Peter ; Mattiussi, Claudio

In: Evolutionary Intelligence, 2008, vol. 1, no. 1, p. 47-62

Rechenstörungen im Kindesalter = Dyscalculia in children

von Aster, M. ; Kucian, K. ; Schweiter, M. ; Martin, E.