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Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning.
Threshold network learning in the presence of equivalences
Shawe-Taylor, J · 1991
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Sample sizes for threshold networks with equivalences
Shawe-Taylor, J · 1995
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Representation theory and invariant neural networks
Wood, J. and Shawe-Taylor, J · 1996
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Some PAC-Bayesian theorems
McAllester, D. A · 1999
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(Not) bounding the true error
Langford, J. and Caruana, R · 2002
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PAC-Bayesian supervised classification: the thermodynamics of statistical learning
Catoni, O · 2007
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Deep learning of invariant features via simulated fixations in video
Zou, W., Zhu, S., Yu, K., and Ng, A. Y · 2012
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Tighter PAC-Bayes bounds through distribution-dependent priors
Lever, G., Laviolette, F., and Shawe-Taylor, J · 2013
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A neural network for factoid question answering over paragraphs
Iyyer, M., Boyd-Graber, J., Claudino, L., Socher, R., and Daumé III, H · 2014
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Predicting effects of noncoding variants with deep learning-based sequence model
Zhou, J. and Troyanskaya, O. G · 2015
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Group equivariant convolutional networks
Cohen, T. S. and Welling, M · 2016
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Adaptive data augmentation for image classification
Fawzi, A., Samulowitz, H., Turaga, D., and Frossard, P · 2016
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Dziugaite, G. K. and Roy, D. M · 2017
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Implicit Regularization in Deep Learning
Neyshabur, B · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Local Group Invariant Representations via Orbit Embeddings
Raj, A., Kumar, A., Mroueh, Y., Fletcher, T., and Schoelkopf, B · 2017
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mané, D., Vasudevan, V., and Le, Q. V · 2018
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Data-dependent PAC-Bayes priors via differential privacy
Dziugaite, G. K. and Roy, D. M · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. and Trivedi, S · 2018
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Learning invariances using the marginal likelihood
van der Wilk, M., Bauer, M., John, S., and Hensman, J · 2018
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Machine health monitoring using local feature-based gated recurrent unit networks
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., and Wang, J · 2018
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Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 2017
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Deep convolutional neural networks and data augmentation for environmental sound classification
Salamon, J. and Bello, J. P · 2017
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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Probabilistic symmetry and invariant neural networks
Bloem-Reddy, B. and Teh, Y. W
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Chen, S., Dobriban, E., and Lee, J. H · 2019
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A general theory of equivariant CNNs on homogeneous spaces
Cohen, T. S., Geiger, M., and Weiler, M · 2019
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A kernel theory of modern data augmentation
Dao, T., Gu, A., Ratner, A. J., Smith, V., De Sa, C., and Ré, C · 2019
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Non-vacuous generalization bounds at the imagenet scale: a PAC-Bayesian compression approach
Zhou, W., Veitch, V., Austern, M., Adams, R. P., and Orbanz, P · 2019
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