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Shift invariance is a critical property of CNNs that improves performance on classification.
Backpropagation applied to handwritten zip code recognition
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Does network width really help adversarial robustness?
Wu, B., Chen, J., Cai, D., He, X., and Gu, Q · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Theory of reproducing kernels and applications
Saitoh, S. and Sawano, Y · 2016
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Gradient descent provably optimizes over-parameterized neural networks
Du, S. S., Zhai, X., Poczos, B., and Singh, A · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
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On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R. R., and Wang, R · 2019
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Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A. and Weiss, Y · 2019
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The convergence rate of neural networks for learned functions of different frequencies
Basri, R., Jacobs, D., Kasten, Y., and Kritchman, S · 2019
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On the inductive bias of neural tangent kernels
Bietti, A. and Mairal, J · 2019
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Batch normalization is a cause of adversarial vulnerability
Galloway, A., Golubeva, A., Tanay, T., Moussa, M., and Taylor, G. W · 2019
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Excessive invariance causes adversarial vulnerability
More data can expand the generalization gap between adversarially robust and standard models
Chen, L., Min, Y., Zhang, M., and Karbasi, A · 2020
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Most relu networks suffer from \ellˆ2 adversarial perturbations
Daniely, A. and Shacham, H · 2020
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On the similarity between the laplace and neural tangent kernels
Geifman, A., Yadav, A., Kasten, Y., Galun, M., Jacobs, D., and Ronen, B · 2020
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Why do deep residual networks generalize better than deep feedforward networks?—a neural tangent kernel perspective
Huang, K., Wang, Y., Tao, M., and Zhao, T · 2020
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Invariance vs robustness of neural networks, 2020
Kamath, S., Deshpande, A., and Subrahmanyam, K. V · 2020
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On translation invariance in cnns: Convolutional layers can exploit absolute spatial location
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Jacobsen, J.-H., Behrmann, J., Zemel, R., and Bethge, M · 2019
Cited alongside, same era.
On the geometry of adversarial examples
Khoury, M. and Hadfield-Menell, D · 2019
Cited alongside, same era.
Enhanced convolutional neural tangent kernels
Li, Z., Wang, R., Yu, D., Du, S. S., Hu, W., Salakhutdinov, R., and Arora, S · 2019
Cited alongside, same era.
Adversarial robustness may be at odds with simplicity
Nakkiran, P · 2019
Cited alongside, same era.
On the spectral bias of neural networks
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., and Courville, A · 2019
Cited alongside, same era.
Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Rony, J., Hafemann, L. G., Oliveira, L. S., Ayed, I. B., Sabourin, R., and Granger, E · 2019
Cited alongside, same era.
Are adversarial examples inevitable?
Shafahi, A., Huang, W. R., Studer, C., Feizi, S., and Goldstein, T · 2019
Cited alongside, same era.
Kayhan, O. S. and Gemert, J. C. v · 2020
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Towards learning convolutions from scratch
Neyshabur, B · 2020
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The pitfalls of simplicity bias in neural networks
Shah, H., Tamuly, K., Raghunathan, A., Jain, P., and Netrapalli, P · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
Wang, H., Wu, X., Huang, Z., and Xing, E. P · 2020
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Tensor programs ii: Neural tangent kernel for any architecture
Yang, G · 2020
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The recurrent neural tangent kernel
Alemohammad, S., Wang, Z., Balestriero, R., and Baraniuk, R · 2021
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Mind the pad – {cnn}s can develop blind spots
Alsallakh, B., Kokhlikyan, N., Miglani, V., Yuan, J., and Reblitz-Richardson, O · 2021
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Truly shift-invariant convolutional neural networks
Chaman, A. and Dokmanic, I · 2021
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Deep neural tangent kernel and laplace kernel have the same {rkhs}
Chen, L. and Xu, S · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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On the adversarial robustness of visual transformers
Shao, R., Shi, Z., Yi, J., Chen, P.-Y., and Hsieh, C.-J · 2021
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The neural tangent link between cnn denoisers and non-local filters
Tachella, J., Tang, J., and Davies, M · 2021
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