Fetching the paper…
Reading the bibliography…
Understanding generalization in deep learning is arguably one of the most important questions in deep learning.
Automated flower classification over a large number of classes
Nilsback, M.-E., and Zisserman, A · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V · 2012
Earlier work this paper cites.
Lin, M., Chen, Q., and Yan, S · 2013
Earlier work this paper cites.
Learning both Weights and Connections for Efficient Neural Networks
Han, S., Pool, J., Tran, J., and Dally, W. J · 2015
Earlier work this paper cites.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Neyshabur, B., Tomioka, R., and Srebro, N · 2015
Earlier work this paper cites.
Norm-based capacity control in neural networks
Neyshabur, B., Tomioka, R., and Srebro, N · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., and Zisserman, A · 2015
Earlier work this paper cites.
Dziugaite, G. K., and Roy, D. M · 2017
Earlier work this paper cites.
Implicit regularization in matrix factorization
Gunasekar, S., Woodworth, B. E., Bhojanapalli, S., Neyshabur, B., and Srebro, N · 2017
Earlier work this paper cites.
Implicit Regularization in Deep Learning
Neyshabur, B · 2017
Earlier work this paper cites.
Exploring generalization in deep learning
Neyshabur, B., Bhojanapalli, S., McAllester, D., and Srebro, N · 2017
Earlier work this paper cites.
Geometry of optimization and implicit regularization in deep learning
Neyshabur, B., Tomioka, R., Salakhutdinov, R., and Srebro, N · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Stronger generalization bounds for deep nets via a compression approach
Arora, S., Ge, R., Neyshabur, B., and Zhang (Alphabetical Order), Y · 2018
Cited alongside, same era.
Cinic-10 is not imagenet or cifar-10, 2018
Darlow, L. N., Crowley, E. J., Antoniou, A., and Storkey, A. J · 2018
Cited alongside, same era.
Data-dependent pac-bayes priors via differential privacy
Dziugaite, G. K., and Roy, D. M · 2018
Cited alongside, same era.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Neyshabur, B., Bhojanapalli, S., and Srebro, N · 2018
Later among the works it cites.
The implicit bias of gradient descent on separable data
Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., and Srebro, N · 2018
Later among the works it cites.
Fantastic generalization measures and where to find them
Jiang, Y., Neyshabur, B., Krishnan, D., Mobahi, H., and Bengio, S · 2019
Later among the works it cites.
Information-theoretic generalization bounds for sgld via data-dependent estimates
Negrea, J., Haghifam, M., Dziugaite, G. K., Khisti, A., and Roy, D. M · 2019
Later among the works it cites.
Towards understanding the role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N · 2019
Later among the works it cites.
Towards task and architecture-independent generalization gap predictors
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Large margin deep networks for classification
Elsayed, G., Krishnan, D., Mobahi, H., Regan, K., and Bengio, S · 2018
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J., and Carbin, M · 2018
Cited alongside, same era.
Characterizing implicit bias in terms of optimization geometry
Gunasekar, S., Lee, J., Soudry, D., and Srebro, N · 2018
Cited alongside, same era.
Implicit bias of gradient descent on linear convolutional networks
Gunasekar, S., Lee, J. D., Soudry, D., and Srebro, N · 2018
Cited alongside, same era.
Predicting the generalization gap in deep networks with margin distributions
Jiang, Y., Krishnan, D., Mobahi, H., and Bengio, S · 2018
Cited alongside, same era.
Rethinking the Value of Network Pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T · 2018
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y
Cited in the paper.
Yak, S., Gonzalvo, J., and Mazzawi, H · 2019
Later among the works it cites.
Fast-rate pac-bayes generalization bounds via shifted rademacher processes
Yang, J., Sun, S., and Roy, D. M · 2019
Later among the works it cites.
The intriguing role of module criticality in the generalization of deep networks
Chatterji, N. S., Neyshabur, B., and Sedghi, H · 2020
Closest in time.
Revisiting Generalization for Deep Learning: PAC-Bayes, Flat Minima, and Generative Models
Dziugaite, G. K · 2020
Closest in time.
Self-distillation amplifies regularization in hilbert space
Mobahi, H., Farajtabar, M., and Bartlett, P. L · 2020
Closest in time.
Observational overfitting in reinforcement learning
Song, X., Jiang, Y., Du, Y., and Neyshabur, B · 2020
Closest in time.