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All organisms make temporal predictions, and their evolutionary fitness level depends on the accuracy of these predictions.
Predictive coding–i
Peter Elias · 1955
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Mandyam Veerambudi Srinivasan, Simon Barry Laughlin, and Andreas Dubs · 1982
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The laplacian pyramid as a compact image code
Peter Burt and Edward Adelson · 1983
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Edward H Adelson and James R Bergen · 1985
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Peter Földiák · 1991
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William T Freeman, Edward H Adelson, et al · 1991
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Eero P Simoncelli and William T Freeman · 1995
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Gregory C DeAngelis, Izumi Ohzawa, and Ralph D Freeman · 1995
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Eero P Simoncelli and David J Heeger · 1998
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Rajesh Rao and Daniel Ruderman · 1998
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Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex
J Hans van Hateren and Dan L Ruderman · 1998
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Anticipation of moving stimuli by the retina
Michael J Berry II, Iman H Brivanlou, Thomas A Jordan, and Markus Meister · 1999
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
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A parametric texture model based on joint statistics of complex wavelet coefficients
Javier Portilla and Eero P Simoncelli · 2000
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A new diamond search algorithm for fast block-matching motion estimation
Shan Zhu and Kai-Kuang Ma · 2000
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Laurenz Wiskott and Terrence J Sejnowski · 2002
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Thomas Wiegand, Gary J Sullivan, Gisle Bjontegaard, and Ajay Luthra · 2003
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Matthias Bethge, Sebastian Gerwinn, and Jakob H Macke · 2007
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Stéphane Mallat · 2008
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The 2017 davis challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alexander Sorkine-Hornung, and Luc Van Gool · 2017
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Deep predictive coding networks for video prediction and unsupervised learning
William Lotter, Gabriel Kreiman, and David Cox · 2017
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The sparse manifold transform
Yubei Chen, Dylan Paiton, and Bruno Olshausen · 2018
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Learning to decompose and disentangle representations for video prediction
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Charles F Cadieu and Bruno A Olshausen · 2012
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Neal Wadhwa, Michael Rubinstein, Frédo Durand, and William T Freeman · 2013
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Deep learning for universal linear embeddings of nonlinear dynamics
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A neural network trained for prediction mimics diverse features of biological neurons and perception
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Forecasting sequential data using consistent koopman autoencoders
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Maximum entropy models from phase harmonic covariances
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Primary visual cortex straightens natural video trajectories
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Biological learning of irreducible representations of commuting transformations
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