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A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>
Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. The 14th Asian Conference on Computer Vision (ACCV 2018) (2018). at <http://accv2018.net/>
Planning Complexity Registers as a Cost in Metacontrol. Journal of Cognitive Neuroscience 30, 1391 - 1404 (2018).
Rational inference of beliefs and desires from emotional expressions. Cognitive Science 42, (2018).
Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes. Cell Reports 25, 2635 - 2642.e5 (2018).
Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115
Relational inductive bias for physical construction in humans and machines. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018) (2018).
Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).
Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).
Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
The statistical shape of geometric reasoning. Scientific Reports 8, (2018).
A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, (2018).
Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).
Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).
Third-Party Preferences for Imitators in Preverbal Infants. Open Mind 2, 61 - 71 (2018).
Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 ( ) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>