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      1 @INPROCEEDINGS{lamperski_2022,
      2   author={Lamperski, Andrew},
      3   booktitle={2022 IEEE 61st Conference on Decision and Control (CDC)},
      4   title={Neural Network Independence Properties with Applications to Adaptive Control},
      5   year={2022},
      6   volume={},
      7   number={},
      8   pages={3365-3370},
      9   doi={10.1109/CDC51059.2022.9992994}
     10 }
     11 
     12 @book{kaltenbacher2008,
     13   title={Iterative Regularization Methods for Nonlinear Ill-Posed Problems},
     14   author={Kaltenbacher, B. and Neubauer, A. and Scherzer, O.},
     15   isbn={9783110208276},
     16   series={Radon Series on Computational and Applied Mathematics},
     17   url={https://books.google.pt/books?id=2bAEqOzyKgAC},
     18   year={2008},
     19   publisher={De Gruyter}
     20 }
     21 
     22 @misc{frischauf2022universal,
     23       title={Universal approximation properties of shallow quadratic neural networks},
     24       author={Leon Frischauf and Otmar Scherzer and Cong Shi},
     25       year={2022},
     26       eprint={2110.01536},
     27       archivePrefix={arXiv},
     28       primaryClass={math.NA}
     29 }
     30 
     31 @article{scherzer2023newton,
     32   title={Newton's methods for solving linear inverse problems with neural network coders},
     33   author={Scherzer, Otmar and Hofmann, Bernd and Nashed, Zuhair},
     34   journal={arXiv preprint arXiv:2303.14058},
     35   year={2023}
     36 }
     37 
     38 @url{code,
     39     title={Result Code},
     40     author={Milutin Popovic},
     41     url={https://github.com/miksa234/gauss_newton_inverse_problems},
     42 }