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114 Publications
2023 | Conference Paper | IST-REx-ID: 13053 | 
Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .
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| arXiv
2023 | Thesis | IST-REx-ID: 13074 |
Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
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2023 | Journal Article | IST-REx-ID: 14320 |
Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., & Serbyn, M. (2023). Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. American Physical Society. https://doi.org/10.1103/physrevb.108.125411
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| arXiv
2023 | Conference Paper | IST-REx-ID: 14410
Tomaszewska, P., & Lampert, C. (2023). On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In International Workshop on Reproducible Research in Pattern Recognition (Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature. https://doi.org/10.1007/978-3-031-40773-4_6
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2023 | Journal Article | IST-REx-ID: 14446 |
Jakubík, J., Phuong, M., Chvosteková, M., & Krakovská, A. (2023). Against the flow of time with multi-output models. Measurement Science Review. Sciendo. https://doi.org/10.2478/msr-2023-0023
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2023 | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
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| arXiv
2023 | Conference Paper | IST-REx-ID: 14921 |
Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
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2023 | Preprint | IST-REx-ID: 15039 |
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
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| arXiv
2022 | Preprint | IST-REx-ID: 12660 |
Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434
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| arXiv
2022 | Preprint | IST-REx-ID: 12662 |
Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499
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| arXiv