Francesca Albertini and Eduardo D. Sontag. For neural networks, function determines form. Technical Report SYCON-92-03, Rutgers Center for Systems and Control, 1992. Expanded version of Albertini and Sontag (1993a). Cited on page 2.
Francesca Albertini and Eduardo D. Sontag. For neural networks, function determines form. Neural Networks, 6(7):975-990, 1993a. Access via Crossref. Cited on pages 2 and 11.
Francesca Albertini and Eduardo D. Sontag. Identifiability of discrete-time neural networks. In Proceedings of the European Control Conference 1993, volume 2, pages 460-465. European Control Association, 1993b. Access via Francesca Albertini. Cited on page 2.
Francesca Albertini and Eduardo D. Sontag. Uniqueness of weights for recurrent nets. In Systems and Networks: Mathematical Theory and Applications: Proceedings of the International Symposium MTNS 1993, volume II, pages 599-602. Akademie Verlag, 1993c. Access via Francesca Albertini or via Eduardo D. Sontag. See also extended version, access via Eduardo D. Sontag. Cited on page 2.
Francesca Albertini, Eduardo D. Sontag, and Vincent Maillot. Uniqueness of weights for neural networks. In Artificial Neural Networks for Speech and Vision, pages 113-125. Chapman & Hall, London, 1993. Proceedings of a workshop held at Rutgers University in 1992. Access via Eduardo D. Sontag. Cited on pages 1 and 2.
Joachim Bona-Pellissier, François Bachoc, and François Malgouyres. Parameter identifiability of a deep feedforward ReLU neural network. 2021. Preprint arXiv:2112.12982 [math.ST]. Cited on page 2.
Johanni Brea, Berfin Şimşek, Bernd Illing, and Wulfram Gerstner. Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape. 2019. Preprint arXiv:1907.02911 [cs.LG]. Cited on page 9.
Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, and Gabriel Kreiman. Frivolous units: Wider networks are not really that wide. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, volume 8, pages 6921-6929. AAAI Press, 2021. Access via Crossref. Cited on page 10.
An Mei Chen and Robert Hecht-Nielsen. On the geometry of feedforward neural network weight spaces. In Second International Conference on Artificial Neural Networks, pages 1-4. IET, 1991. Access via IEEE Xplore. Cited on page 2.
An Mei Chen, Haw-minn Lu, and Robert Hecht-Nielsen. On the geometry of feedforward neural network error surfaces. Neural Computation, 5(6):910-927, 1993. Access via Crossref. Cited on page 2.
Matthew Farrugia-Roberts. *Structural Degeneracy in Neural Networks*. Master's thesis, School of Computing and Information Systems, The University of Melbourne, 2022. Access via Matthew Farrugia-Roberts. Cited on page 10.
Matthew Farrugia-Roberts. Computational complexity of determining proximity to compressible neural networks. 2023. Preprint arXiv:2306.02834 [cs.LG]. Cited on pages 1, 9, and 10.
Charles Fefferman. Reconstructing a neural net from its output. Revista Matematica Iberoamericana, 10(3):507-555, 1994. Access via Crossref. Cited on page 2.
Charles Fefferman and Scott Markel. Recovering a feed-forward net from its output. In Advances in Neural Information Processing Systems 6, pages 335-342. Morgan Kaufmann, 1993. Access via NeurIPS. Cited on page 2.
Kenji Fukumizu. A regularity condition of the information matrix of a multilayer perceptron network. *Neural Networks*, 9(5):871-879, 1996. Access via Crossref. Cited on pages 1, 10, and 13.
Kenji Fukumizu and Shun-ichi Amari. Local minima and plateaus in hierarchical structures of multilayer perceptrons. *Neural Networks*, 13(3):317–327, 2000. Access via Crossref. Cited on pages 2, 5, and 9.