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50 Publications

2021 | Published | Conference Paper | IST-REx-ID: 10595 | OA
Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks
Q. Nguyen, M. Mondelli, G.F. Montufar, in:, M. Meila, T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 10597 | OA
Sparse multi-decoder recursive projection aggregation for Reed-Muller codes
D. Fathollahi, N. Farsad, S.A. Hashemi, M. Mondelli, in:, 2021 IEEE International Symposium on Information Theory, Institute of Electrical and Electronics Engineers, 2021, pp. 1082–1087.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 10598 | OA
Approximate message passing with spectral initialization for generalized linear models
M. Mondelli, R. Venkataramanan, in:, A. Banerjee, K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2021, pp. 397–405.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 10599 | OA
Successive syndrome-check decoding of polar codes
S.A. Hashemi, M. Mondelli, J. Cioffi, A. Goldsmith, in:, Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, Institute of Electrical and Electronics Engineers, 2021, pp. 943–947.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2020 | Published | Conference Paper | IST-REx-ID: 8536 | OA
Simplified successive cancellation decoding of polar codes has sublinear latency
M. Mondelli, S.A. Hashemi, J. Cioffi, A. Goldsmith, in:, IEEE International Symposium on Information Theory - Proceedings, IEEE, 2020.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2020 | Published | Journal Article | IST-REx-ID: 6748 | OA
Analysis of a two-layer neural network via displacement convexity
A. Javanmard, M. Mondelli, A. Montanari, Annals of Statistics 48 (2020) 3619–3642.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2020 | Published | Conference Paper | IST-REx-ID: 9198 | OA
Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks
A. Shevchenko, M. Mondelli, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 8773–8784.
[Published Version] View | Files available | arXiv
 
2020 | Published | Conference Paper | IST-REx-ID: 9221 | OA
Global convergence of deep networks with one wide layer followed by pyramidal topology
Q. Nguyen, M. Mondelli, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 11961–11972.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Journal Article | IST-REx-ID: 6750 | OA
Rate-flexible fast polar decoders
S.A. Hashemi, C. Condo, M. Mondelli, W.J. Gross, IEEE Transactions on Signal Processing 67 (2019).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2019 | Published | Journal Article | IST-REx-ID: 7007 | OA
A new coding paradigm for the primitive relay channel
M. Mondelli, S.H. Hassani, R. Urbanke, Algorithms 12 (2019).
[Published Version] View | Files available | DOI | arXiv
 

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