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Publications about 'Neural Networks'
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, 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]


  4. 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]


  5. V. Centorrino, F. Bullo, and G. Russo. Modelling and Contractivity of Neural-Synaptic Networks with Hebbian Learning. Automatica, July 2022. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  6. 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. S. Jaffe, A. K. Singh, and F. Bullo. IDKM: Memory Efficient Neural Network Quantization via Implicit Differentiable $k$-Means. In ICLR 2023 Workshop on Sparsity in Neural Networks, May 2023. Keyword(s): Neural Networks. [bibtex-entry]


  2. F. Seccamonte, A. K. Singh, and F. Bullo. Inference of Infrastructure Network Flows via Physics-Inspired Implicit Neural Networks. In IEEE Conf. on Control Technology and Applications, Bridgetown, Barbados, August 2023. Note: To appear. Keyword(s): Neural Networks. [bibtex-entry]


  3. V. Centorrino, F. Bullo, and G. Russo. Contraction Analysis of Hopfield Neural Networks with Hebbian Learning. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. 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]


  5. 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]


  6. 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]


  7. 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]


  8. 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]


  9. 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]



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