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On the Benefits of Invariance in Neural Networks
[article]
2020
arXiv
pre-print
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. While the literature contains a variety of methods to incorporate invariance into models, theoretical understanding is poor and there is no way to assess when one method should be preferred over another. In this work, we analyze the benefits and limitations of two widely used approaches in deep learning
arXiv:2005.00178v1
fatcat:45lmcynbjnertgapp6x2ok2yu4