Elena-Alexandra Peste
7 Publications
2023 | Published | Conference Paper | IST-REx-ID: 13053 | 
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda , 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 15363 |
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
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2023 | Published | Thesis | IST-REx-ID: 13074 |
A. Krumes, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 14771 |
E. B. Iofinova, A. Krumes, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
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2022 | Published | Conference Paper | IST-REx-ID: 12299 |
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11458 |
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
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2021 | Published | Journal Article | IST-REx-ID: 10180 |
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and A. Krumes, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. ML Research Press, pp. 1–124, 2021.
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Grants
7 Publications
2023 | Published | Conference Paper | IST-REx-ID: 13053 |
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda , 2023.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 15363 |
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
[Published Version]
View
| Files available
| arXiv
2023 | Published | Thesis | IST-REx-ID: 13074 |
A. Krumes, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
[Published Version]
View
| Files available
| DOI
2023 | Published | Conference Paper | IST-REx-ID: 14771 |
E. B. Iofinova, A. Krumes, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12299 |
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11458 |
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2021 | Published | Journal Article | IST-REx-ID: 10180 |
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and A. Krumes, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. ML Research Press, pp. 1–124, 2021.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv