mlsanityData to 14 Oct 2026

Bisimulations for Neural Network Reduction

arXiv 2110.03726 ↗First posted 7 Oct 2021cs.LG cs.FLcited by 6

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Abstract (arXiv)

We present a notion of bisimulation that induces a reduced network which is semantically equivalent to the given neural network. We provide a minimization algorithm to construct the smallest bisimulation equivalent network. Reductions that construct bisimulation equivalent neural networks are limited in the scale of reduction. We present an approximate notion of bisimulation that provides semantic closeness, rather than, semantic equivalence, and quantify semantic deviation between the neural networks that are approximately bisimilar. The latter provides a trade-off between the amount of reduction and deviations in the semantics.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 7 Oct 2021.

Builds on

11 of 20 references.

References · 11

  1. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  2. 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 415
  3. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 301
  4. 2017Output Reachable Set Estimation and Verification for Multi-Layer Neural Networkscited by 80
  5. 2019An Abstraction-Based Framework for Neural Network Verificationcited by 68
  6. 2020Abstraction based Output Range Analysis for Neural Networkscited by 34
  7. 2020DeepAbstract: Neural Network Abstraction for Accelerating Verificationcited by 31
  8. 2018Verification for Machine Learning, Autonomy, and Neural Networks Surveycited by 24
  9. 2020Abstract Neural Networkscited by 11
  10. 2018A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretabilitycited by 8
  11. 2017A Unified View of Piecewise Linear Neural Network Verificationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2023Verifying Generalization in Deep Learningcited by 11
  2. 2023Fully Automatic Neural Network Reduction for Formal Verificationcited by 6
  3. 2023DelBugV: Delta-Debugging Neural Network Verifierscited by 3
  4. 2022Neural Networks Reduction via Lumpingcited by 2
  5. 2023Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networkscited by 0
  6. 2026Architecture Before the Formula: Training-Dependent Computational Semantics in Transformerscited by 0