An efficient algorithm for learning invariance in adaptive classifiers
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An efficient algorithm for learning invariance in adaptive classifiers. Abstract: In many machine learning applications, one has not only training data but ...
The key idea in this paper is that it is possible to directly learn the effect on the output of distorting the input, independently from learning the ...
TL;DR: This work presents a systematic, unifying taxonomy to categorize existing methods, distinguishing methods that affect data, network architectures, error ...
Mar 5, 2024 · This paper introduces a novel optimization algorithm named KATE, which presents a scale-invariant adaptation of the well-known AdaGrad algorithm ...
Missing: classifiers. | Show results with:classifiers.
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Sets of uncorrelated dictionary elements can be constructed using the matching of time invariant filters (MoTIF) algorithm presented in [18] . In the numerical ...
Missing: classifiers. | Show results with:classifiers.
Aug 30, 2001 · The model is an effective means to explicitly incorporate invariance ... An Efficient Algorithm for Learning Invariances in Adaptive Classifiers.
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization ...
In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we ...
Missing: algorithm classifiers.
We propose a differentiable Kronecker-factored. Laplace approximation to the marginal likelihood as our objective, which can be optimised without human ...
Feb 20, 2019 · In this paper, we resolve the tuning problem by proposing online algorithms making predictions which are invariant under arbitrary rescaling of ...
Missing: classifiers. | Show results with:classifiers.