Properties of artificial networks evolved to contend with natural spectra

By Yaniv Morgenstern, M. Rostami, D. Purves. 2014

20 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Detection and Continual Learning of Novel Face Presentation Attacks (2021, methodology): “Similar to most sub-fields in computer vision, advances in deep learning, which is inspired by the nervous system [17], have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [1, 7, 12, ”
Generative Continual Concept Learning (2019, background): “For this reason and despite emergence of behaviors similar to the nervous system in deep nets [2], adapting a deep neural network to learn a concept in a new domain usually requires model retraining from scratch which is conditioned on the availability of a large number of labeled samples in the new”
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (2019, background): “This may seem natural as these networks are the best accessible tools that mimic the human nervous system [Morgenstern et al., 2014].”
Learning Transferable Knowledge Through Embedding Spaces (2019, background): “The recent breakthrough of deep learning may seem natural as these networks are designed to mimic the human nervous system, and to some extent, they do [202].”