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Publications about 'Neural Networks'
Thesis
  1. S. Jaffe. Dynamical Systems and Neural Networks: From Implicit Models to Memory Retrieval. PhD thesis, Computer Science Department, University of California at Santa Barbara, September 2024. Keyword(s): Contraction Theory, Network Systems, Machine Learning. [bibtex-entry]


Articles in journal, book chapters
  1. V. Centorrino, F. Bullo, and G. Russo. Modelling and Contractivity of Neural-Synaptic Networks with Hebbian Learning. Automatica, 164:111636, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On Weakly Contracting Dynamics for Convex Optimization. IEEE Control Systems Letters, 8:1745-1750, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive Competitive Networks for Sparse Reconstruction. Neural Computation, 36(6):1163–1197, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. A. Davydov and F. Bullo. Perspectives on Contractivity in Control, Optimization and Learning. IEEE Control Systems Letters, 8:2087-2098, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  5. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks. IEEE Transactions on Automatic Control, 2024. Note: To appear. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  6. 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]


  7. 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: To appear. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  8. 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]


Conference articles
  1. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Biologically Plausible Neural Networks for Sparse Reconstruction: A Normative Framework. In Workshop “Mathematics for Artificial Intelligence and Machine Learning”, Milan, Italy, january 2024. Note: Oral Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  2. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Towards a Top/Down Normative Framework for a Biologically Plausible Explanation of Neural Circuits: Application to Sparse Reconstruction Problems. In 5th International Convention on the Mathematics of Neuroscience and AI, Rome, Italy, May 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Contractivity of Symmetric Neural Networks for Non-negative Sparse Approximation. In CCS/Italy 2023 Italian Regional Conference on Complex Systems, Naples, Italy, october 2023. Note: Poster Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


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


  5. 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. Keyword(s): Neural Networks. [bibtex-entry]


  6. 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]


  7. 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]


  8. 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]


  9. 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]


  10. 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]


  11. 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]


  12. 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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