Diyuan Wu

3 Publications

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[3]
2025 | Published | Conference Paper | IST-REx-ID: 21326 | OA
Wu, Diyuan, and Marco Mondelli. “Neural Collapse beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime.” Proceedings of the 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 67499–536.
[Published Version] View | Files available | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, et al. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14924 | OA
Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” Transactions on Machine Learning Research, ML Research Press, 2023.
[Published Version] View | Download Published Version (ext.) | arXiv
 
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3 Publications

Mark all

[3]
2025 | Published | Conference Paper | IST-REx-ID: 21326 | OA
Wu, Diyuan, and Marco Mondelli. “Neural Collapse beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime.” Proceedings of the 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 67499–536.
[Published Version] View | Files available | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, et al. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14924 | OA
Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” Transactions on Machine Learning Research, ML Research Press, 2023.
[Published Version] View | Download Published Version (ext.) | arXiv
 
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Citation Style: MLA

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