mlsanityData to 7 Oct 2026

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

arXiv 1905.10259 ↗First posted 24 May 2019cs.LG stat.MLcited by 38

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Abstract (arXiv)

We present a comprehensive study of multilayer neural networks with binary activation, relying on the PAC-Bayesian theory. Our contributions are twofold: (i) we develop an end-to-end framework to train a binary activated deep neural network, (ii) we provide nonvacuous PAC-Bayesian generalization bounds for binary activated deep neural networks. Our results are obtained by minimizing the expected loss of an architecture-dependent aggregation of binary activated deep neural networks. Our analysis inherently overcomes the fact that binary activation function is non-differentiable. The performance of our approach is assessed on a thorough numerical experiment protocol on real-life datasets.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 13; 2023: 7; 2024: 3; 2025: 4; 2026: 3.

38 papers.

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  1. 2021User-friendly introduction to PAC-Bayes boundscited by 234
  2. 2022Enhancing Adversarial Training with Second-Order Statistics of Weightscited by 42
  3. 2023A unified recipe for deriving (time-uniform) PAC-Bayes boundscited by 25
  4. 2022PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingalescited by 23
  5. 2022Non-Vacuous Generalisation Bounds for Shallow Neural Networkscited by 22
  6. 2021Learning PAC-Bayes Priors for Probabilistic Neural Networkscited by 21
  7. 2022On PAC-Bayesian reconstruction guarantees for VAEscited by 20
  8. 2021On Margins and Derandomisation in PAC-Bayescited by 19
  9. 2022PAC-Bayesian Offline Contextual Bandits With Guaranteescited by 19
  10. 2021Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Votecited by 13
  11. 2023Learning via Wasserstein-Based High Probability Generalisation Boundscited by 11
  12. 2021Wide stochastic networks: Gaussian limit and PAC-Bayesian trainingcited by 10
26 more
  1. 2022Generalisation under gradient descent via deterministic PAC-Bayescited by 9
  2. 2022Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficultycited by 9
  3. 2021Conditionally Gaussian PAC-Bayescited by 7
  4. 2022On Margins and Generalisation for Voting Classifierscited by 7
  5. 2022PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparisoncited by 7
  6. 2024Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measurescited by 7
  7. 2022Weight Expansion: A New Perspective on Dropout and Generalizationcited by 6
  8. 2023Improving Generalization of Complex Models under Unbounded Loss Using PAC-Bayes Boundscited by 6
  9. 2023Comparing Comparators in Generalization Boundscited by 5
  10. 2024Sample Compression Unleashed: New Generalization Bounds for Real Valued Lossescited by 4
  11. 2021Multi-class Probabilistic Bounds for Self-learningcited by 3
  12. 2022Controlling Multiple Errors Simultaneously with a PAC-Bayes Boundcited by 3
  13. 2022Seeking Interpretability and Explainability in Binary Activated Neural Networkscited by 3
  14. 2023Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisationcited by 3
  15. 2025A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votescited by 3
  16. 2023Federated Learning with Nonvacuous Generalisation Boundscited by 2
  17. 2024Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theorycited by 2
  18. 2025Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimizationcited by 2
  19. 2025Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifiercited by 2
  20. 2021PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representationscited by 1
  21. 2022Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learningcited by 1
  22. 2023A note on regularised NTK dynamics with an application to PAC-Bayesian trainingcited by 1
  23. 2025Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?cited by 1
  24. 2026Smoothness-Based Derandomization of PAC-Bayes Boundscited by 1
  25. 2026PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoderscited by 1
  26. 2026On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithmcited by 0