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Residual Pathway Priors for Soft Equivariance Constraints
[article]
2021
arXiv
pre-print
There is often a trade-off between building deep learning systems that are expressive enough to capture the nuances of the reality, and having the right inductive biases for efficient learning. We introduce Residual Pathway Priors (RPPs) as a method for converting hard architectural constraints into soft priors, guiding models towards structured solutions, while retaining the ability to capture additional complexity. Using RPPs, we construct neural network priors with inductive biases for
arXiv:2112.01388v1
fatcat:yiyyyn6435bgdmgjen52phb3pu