Fetching the paper…
Reading the bibliography…
In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
David H Hubel and Torsten N Wiesel · 1962
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Kunihiko Fukushima · 1980
Earlier work this paper cites.
Preattentive texture discrimination with early vision mechanisms
Jitendra Malik and Pietro Perona · 1990
Earlier work this paper cites.
The strength of weak learnability
Robert E Schapire · 1990
Earlier work this paper cites.
Untersuchungen zu dynamischen neuronalen netzen
Sepp Hochreiter · 1991
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
Earlier work this paper cites.
Boosting and other ensemble methods
Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, and Vladimir Vapnik · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A feedforward architecture accounts for rapid categorization
Thomas Serre, Aude Oliva, and Tomaso Poggio · 2007
Cited alongside, same era.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Later among the works it cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Later among the works it cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Cited alongside, same era.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Closest in time.
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger · 2016
Closest in time.