Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
71 Publications
2021 | Published | Conference Paper | IST-REx-ID: 10595 | 
Nguyen, Q., Mondelli, M., & Montufar, G. F. (2021). Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In M. Meila & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10599 |
Hashemi, S. A., Mondelli, M., Cioffi, J., & Goldsmith, A. (2021). Successive syndrome-check decoding of polar codes. In Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers (Vol. 2021–October, pp. 943–947). Virtual, Pacific Grove, CA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEEECONF53345.2021.9723394
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10597 |
Fathollahi, D., Farsad, N., Hashemi, S. A., & Mondelli, M. (2021). Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In 2021 IEEE International Symposium on Information Theory (pp. 1082–1087). Virtual, Melbourne, Australia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/isit45174.2021.9517887
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 13146 |
Nguyen, Quynh, Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International Conference on Machine Learning 139. 2021
[Published Version]
View
| Files available
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10598 |
Mondelli, M., & Venkataramanan, R. (2021). Approximate message passing with spectral initialization for generalized linear models. In A. Banerjee & K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 397–405). Virtual, San Diego, CA, United States: ML Research Press.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2020 | Published | Journal Article | IST-REx-ID: 6748 |
Javanmard, A., Mondelli, M., & Montanari, A. (2020). Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. Institute of Mathematical Statistics. https://doi.org/10.1214/20-AOS1945
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 8536 |
Mondelli, Marco, Simplified successive cancellation decoding of polar codes has sublinear latency. IEEE International Symposium on Information Theory - Proceedings 2020-June. 2020
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 9221 |
Nguyen, Quynh, Global convergence of deep networks with one wide layer followed by pyramidal topology. 34th Conference on Neural Information Processing Systems 33. 2020
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 9198 |
Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784). ML Research Press.
[Published Version]
View
| Files available
| arXiv
2019 | Published | Journal Article | IST-REx-ID: 7007 |
Mondelli, M., Hassani, S. H., & Urbanke, R. (2019). A new coding paradigm for the primitive relay channel. Algorithms. MDPI. https://doi.org/10.3390/a12100218
[Published Version]
View
| Files available
| DOI
| arXiv
2019 | Published | Journal Article | IST-REx-ID: 6750 |
Hashemi, S. A., Condo, C., Mondelli, M., & Gross, W. J. (2019). Rate-flexible fast polar decoders. IEEE Transactions on Signal Processing. IEEE. https://doi.org/10.1109/TSP.2019.2944738
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv