@article {2034,
	title = {Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex},
	year = {2016},
	month = {04/2016},
	abstract = {<p>We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset.</p>
},
	author = {Qianli Liao and Tomaso Poggio}
}
