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On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing
The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear.
Equivariant and Invariant Reynolds Networks
We construct learning models based on the reductive Reynolds operator called equivariant and invariant Reynolds networks (ReyNets) and prove that they have universal approximation property.
Reynolds Equivariant and Invariant Networks
To overcome this difficulty, we consider representing the Reynolds operator as a sum over a subset instead of a sum over the whole group.
Group Equivariant Conditional Neural Processes
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space.
Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces
To describe the effect of invariance and equivariance on generalization, we develop a notion of a \textit{quotient feature space}, which measures the effect of group actions for the properties.
