Understanding deep learning requires rethinking generalization.

Articles citing Understanding deep learning requires rethinking generalization.

Title / Author	Year
Recent advances in convolutional neural networks J Gu, Z Wang, J Kuen, L Ma, A Shahroudy, B Shuai, T Liu, X Wang, ... Pattern Recognition 77, 354-377	2018
mixup: Beyond empirical risk minimization H Zhang, M Cisse, YN Dauphin, D Lopez-Paz arXiv preprint arXiv:1710.09412	2017
On calibration of modern neural networks C Guo, G Pleiss, Y Sun, KQ Weinberger arXiv preprint arXiv:1706.04599	2017
A survey of methods for explaining black box models R Guidotti, A Monreale, S Ruggieri, F Turini, F Giannotti, D Pedreschi ACM computing surveys (CSUR) 51 (5), 1-42	2018
Opening the black box of deep neural networks via information R Shwartz-Ziv, N Tishby arXiv preprint arXiv:1703.00810	2017
Deep image prior D Ulyanov, A Vedaldi, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Random Erasing Data Augmentation. Z Zhong, L Zheng, G Kang, S Li, Y Yang AAAI, 13001-13008	2020
Deep reinforcement learning: An overview Y Li arXiv preprint arXiv:1701.07274	2017
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in Neural Information Processing Systems, 6240-6249	2017
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems, 5947-5956	2017
Visualizing the loss landscape of neural nets H Li, Z Xu, G Taylor, C Studer, T Goldstein Advances in Neural Information Processing Systems, 6389-6399	2018
Neural tangent kernel: Convergence and generalization in neural networks A Jacot, F Gabriel, C Hongler Advances in neural information processing systems, 8571-8580	2018
Don't decay the learning rate, increase the batch size SL Smith, PJ Kindermans, C Ying, QV Le arXiv preprint arXiv:1711.00489	2017
A closer look at memorization in deep networks D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... arXiv preprint arXiv:1706.05394	2017
Variational dropout sparsifies deep neural networks D Molchanov, A Ashukha, D Vetrov arXiv preprint arXiv:1701.05369	2017
Taskonomy: Disentangling task transfer learning AR Zamir, A Sax, W Shen, LJ Guibas, J Malik, S Savarese Proceedings of the IEEE conference on computer vision and pattern …	2018
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning A Mathis, P Mamidanna, KM Cury, T Abe, VN Murthy, MW Mathis, ... Nature neuroscience 21 (9), 1281-1289	2018
Entropy-sgd: Biasing gradient descent into wide valleys P Chaudhari, A Choromanska, S Soatto, Y LeCun, C Baldassi, C Borgs, ... Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124018	2019
Train longer, generalize better: closing the generalization gap in large batch training of neural networks E Hoffer, I Hubara, D Soudry Advances in Neural Information Processing Systems, 1731-1741	2017
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh arXiv preprint arXiv:1810.02054	2018

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