Fetching the paper…
Reading the bibliography…
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR) 15(1):1929–1958
1958
Earlier work this paper cites.
Song S, Miller KD, Abbott LF (2000) Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience 3(9):919
2000
Earlier work this paper cites.
Hebb DO (2005) The organization of behavior: A neuropsychological theory. Psychology Press
2005
Earlier work this paper cites.
Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. Tech. Rep. 7694, California Institute of Technology, URL http://authors.library.caltech.edu/7694
2007
Earlier work this paper cites.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2009
Earlier work this paper cites.
Krizhevsky A, Hinton G, et al. (2009) Learning multiple layers of features from tiny images. Citeseer
2009
Earlier work this paper cites.
2012
Earlier work this paper cites.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS)
2012
Earlier work this paper cites.
Soomro K, Zamir AR, Shah M (2012) UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:12120402
2012
Earlier work this paper cites.
Ba J, Frey B (2013) Adaptive dropout for training deep neural networks. In: Advances in Neural Information Processing Systems (NIPS)
2013
Earlier work this paper cites.
Baldi P, Sadowski PJ (2013) Understanding dropout. Advances in Neural Information Processing Systems (NIPS)
2013
Earlier work this paper cites.
Wager S, Wang S, Liang PS (2013) Dropout training as adaptive regularization. In: Advances in Neural Information Processing Systems (NIPS)
2013
Earlier work this paper cites.
Wan L, Zeiler M, Zhang S, Le Cun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: Proc. International Conference on Machine Learning (ICML)
2013
Cited alongside, same era.
Wang S, Manning C (2013) Fast dropout training. In: Proc. International Conference on Machine Learning (ICML), pp 118–126
2013
Cited alongside, same era.
Rennie SJ, Goel V, Thomas S (2014) Annealed dropout training of deep networks. In: Spoken Language Technology Workshop (SLT), 2014 IEEE, IEEE, pp 159–164
2014
Cited alongside, same era.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556
2014
Cited alongside, same era.
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop
2015
Gal Y, Hron J, Kendall A (2017) Concrete dropout. In: Advances in Neural Information Processing Systems (NIPS)
2017
Later among the works it cites.
Kang G, Li J, Tao D (2017) Shakeout: A new approach to regularized deep neural network training. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
2017
Later among the works it cites.
Ma S, Bargal SA, Zhang J, Sigal L, Sclaroff S (2017) Do less and achieve more: Training CNNs for action recognition utilizing action images from the web. Pattern Recognition
2017
Later among the works it cites.
Morerio P, Cavazza J, Volpi R, Vidal R, Murino V (2017) Curriculum dropout. In: Proc. IEEE International Conference on Computer Vision (ICCV)
2017
Later among the works it cites.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proc. IEEE International Conference on Computer Vision (ICCV)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems (NIPS)
2015
Cited alongside, same era.
Wu H, Gu X (2015) Towards dropout training for convolutional neural networks. Neural Networks 71:1–10
2015
Cited alongside, same era.
Li Z, Gong B, Yang T (2016) Improved dropout for shallow and deep learning. In: Advances in Neural Information Processing Systems (NIPS)
2016
Cited alongside, same era.
Zagoruyko S, Komodakis N (2016) Wide residual networks. Proc British Machine Vision Conference (BMVC)
2016
Cited alongside, same era.
Zhang J, Lin Z, Brandt J, Shen X, Sclaroff S (2016) Top-down neural attention by excitation backprop. In: Proc. European Conference on Computer Vision (ECCV)
2016
Cited alongside, same era.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Zhang J, Bargal SA, Lin Z, Brandt J, Shen X, Sclaroff S (2017) Top-down neural attention by excitation backprop. International Journal of Computer Vision (IJCV) pp 1–19
2017
Later among the works it cites.
Achille A, Soatto S (2018) Information dropout: Learning optimal representations through noisy computation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
2018
Closest in time.
Ghiasi G, Lin TY, Le QV (2018) Dropblock: A regularization method for convolutional networks. In: Advances in Neural Information Processing Systems (NIPS)
2018
Closest in time.
Gomez AN, Zhang I, Swersky K, Gal Y, Hinton GE (2018) Targeted dropout. In: NIPS Compact Deep Neural Network Representation with Industrial Applications Workshop
2018
Closest in time.
Miconi T, Clune J, Stanley KO (2018) Differentiable plasticity: training plastic neural networks with backpropagation. arXiv preprint arXiv:180402464
2018
Closest in time.
Mittal D, Bhardwaj S, Khapra MM, Ravindran B (2018) Recovering from random pruning: On the plasticity of deep convolutional neural networks. Winter Conference on Applications of Computer Vision
2018
Closest in time.