Elena-Alexandra Peste
7 Publications
2023 | Published | Conference Paper | IST-REx-ID: 13053 | 
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
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2023 | Published | Conference Paper | IST-REx-ID: 15363 |
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
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2023 | Published | Thesis | IST-REx-ID: 13074 |
Krumes, 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 | Published | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Krumes, 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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2022 | Published | Conference Paper | IST-REx-ID: 12299 |
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
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2021 | Published | Conference Paper | IST-REx-ID: 11458 |
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
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2021 | Published | Journal Article | IST-REx-ID: 10180 |
Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., & Krumes, A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. ML Research Press.
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Grants
7 Publications
2023 | Published | Conference Paper | IST-REx-ID: 13053 |
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 15363 |
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
[Published Version]
View
| Files available
| arXiv
2023 | Published | Thesis | IST-REx-ID: 13074 |
Krumes, A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
[Published Version]
View
| Files available
| DOI
2023 | Published | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Krumes, 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
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12299 |
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11458 |
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2021 | Published | Journal Article | IST-REx-ID: 10180 |
Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., & Krumes, A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. ML Research Press.
[Published Version]
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| Files available
| Download Published Version (ext.)
| arXiv