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PAC-Bayesian Margin Bounds for Convolutional Neural Networks

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arxiv 1801.00171 v2 pith:KFOSO4WA submitted 2017-12-30 cs.LG stat.ML

Konstantinos Pitas , Mike Davies , Pierre Vandergheynst This is my paper · ORCID
classification cs.LGstat.ML
keywords convolutionalnetworksneuralpac-bayesianadaptanalyzedapproachbeen
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Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers. We adapt this approach to the convolutional setting.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Margin-Adaptive Confidence Ranking for Reliable LLM Judgement

    cs.LG 2026-05 unverdicted novelty 5.0 of 10

    Learning a margin-based confidence ranker for LLM judges improves agreement-target success in cascaded selective evaluation compared to heuristic confidence scores.

  2. PAC-Bayes with Backprop

    cs.LG 2019-08 reject novelty 5.0 of 10

    Training neural networks with PAC-Bayes objectives yields MNIST test error of 1.4% and a non-vacuous risk bound of 2.3%, much tighter than prior PAC-Bayes certificates.

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