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Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models.
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Esteban G Tabak and Cristina V Turner · 2013
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Meta-learning symmetries by reparameterization
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Adam: A method for stochastic optimization
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General e ( 2 ) e(2) -equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
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Learning invariances in neural networks
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Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia · 2017
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Local group invariant representations via orbit embeddings
Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, and Bernhard Schölkopf · 2017
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Probabilistic spatial transformers for bayesian data augmentation
Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H Madsen, and Søren Hauberg · 2020
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
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Deep neural networks as point estimates for deep gaussian processes
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, and Nicolas Durrande · 2021
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
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Scalable marginal likelihood estimation for model selection in deep learning
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Topographic vaes learn equivariant capsules
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Last layer marginal likelihood for invariance learning
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