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Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural Networks.”https://doi.org/10.15479/at:ista:13074__Institute of Science and Technology Austria, 2023.https://doi.org/10.15479/at:ista:13074 .

Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”https://doi.org/10.1109/cvpr52729.2023.02334__In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.https://doi.org/10.1109/cvpr52729.2023.02334 .

Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?”https://doi.org/10.1109/cvpr52688.2022.01195__In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/10.1109/cvpr52688.2022.01195 .

Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.

Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Curran Associates, 2021.