BACK TO INDEX

Publications of A. Davydov
Articles in journal, book chapters
  1. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Contractivity of Competitive Neural Networks for Sparse Reconstruction. Technical Report, September 2023. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Euclidean Contractivity of Neural Networks with Symmetric Weights. IEEE Control Systems Letters, 7:1724-1729, 2023. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. A. Davydov, V. Centorrino, A. Gokhale, G. Russo, and F. Bullo. Contracting Dynamics for Time-Varying Convex Optimization. IEEE Transactions on Automatic Control, June 2023. Note: Submitted. Keyword(s): Contraction Theory. [bibtex-entry]


  4. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory and Applications. Journal of Machine Learning Research, June 2023. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  5. A. Gokhale, A. Davydov, and F. Bullo. Contractivity of Distributed Optimization and Nash Seeking Dynamics. IEEE Control Systems Letters, September 2023. Note: Submitted. [bibtex-entry]


  6. S. Jafarpour, A. Davydov, and F. Bullo. Non-Euclidean Contraction Theory for Monotone and Positive Systems. IEEE Transactions on Automatic Control, 68(9):5653-5660, 2023. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  7. A. V. Proskurnikov, A. Davydov, and F. Bullo. The Yakubovich S-Lemma Revisited: Stability and Contractivity in Non-Euclidean Norms. SIAM Journal on Control and Optimization, 61(4):1955-1978, 2023. Keyword(s): Contraction Theory. [bibtex-entry]


  8. A. Davydov, S. Jafarpour, and F. Bullo. Non-Euclidean Contraction Theory for Robust Nonlinear Stability. IEEE Transactions on Automatic Control, 67(12):6667-6681, 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  9. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks. IEEE Transactions on Automatic Control, September 2022. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


Conference articles
  1. A. Davydov, S. Jafarpour, M. Abate, F. Bullo, and S. Coogan. Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contractivity of Recurrent Neural Networks. In American Control Conference, Atlanta, USA, pages 1527-1534, May 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. S. Jafarpour, M. Abate, A. Davydov, F. Bullo, and S. Coogan. Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach. In Learning for Dynamics and Control Conference, June 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  5. S. Jafarpour, A. Davydov, M. Abate, F. Bullo, and S. Coogan. Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach. In ICML Workshop on Formal Verification of Machine Learning, July 2022. Keyword(s): Contraction Theory. [bibtex-entry]


  6. F. Bullo, P. Cisneros-Velarde, A. Davydov, and S. Jafarpour. From Contraction Theory to Fixed Point Algorithms on Riemannian and non-Euclidean Spaces. In IEEE Conf. on Decision and Control, December 2021. Keyword(s): Contraction Theory. [bibtex-entry]


  7. S. Jafarpour, A. Davydov, A. V. Proskurnikov, and F. Bullo. Robust Implicit Networks via Non-Euclidean Contractions. In Advances in Neural Information Processing Systems, December 2021. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



BACK TO INDEX