3 Publications

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[3]
2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Kögler, Kevin, et al. “Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 24964–5015.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[2]
2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko, Alexander, et al. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 12540 | OA
Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022.
[Published Version] View | Files available
 

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3 Publications

Mark all

[3]
2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Kögler, Kevin, et al. “Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 24964–5015.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[2]
2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko, Alexander, et al. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 12540 | OA
Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022.
[Published Version] View | Files available
 

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Citation Style: MLA

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