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Alexandra Peste
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2020 – today
- 2024
- [j2]Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [c6]Eugenia Iofinova, Alexandra Peste, Dan Alistarh:
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CVPR 2023: 24364-24373 - [c5]Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh:
CrAM: A Compression-Aware Minimizer. ICLR 2023 - [c4]Mher Safaryan, Alexandra Peste, Dan Alistarh:
Knowledge Distillation Performs Partial Variance Reduction. NeurIPS 2023 - [i10]Eugenia Iofinova, Alexandra Peste, Dan Alistarh:
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CoRR abs/2304.12622 (2023) - [i9]Mher Safaryan, Alexandra Peste, Dan Alistarh:
Knowledge Distillation Performs Partial Variance Reduction. CoRR abs/2305.17581 (2023) - [i8]Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. CoRR abs/2308.02060 (2023) - [i7]Paniz Halvachi, Alexandra Peste, Dan Alistarh, Christoph H. Lampert:
ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment. CoRR abs/2312.06872 (2023) - 2022
- [c3]Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh:
How Well Do Sparse ImageNet Models Transfer? CVPR 2022: 12256-12266 - [i6]Alexandra Peste, Adrian Vladu, Dan Alistarh, Christoph H. Lampert:
CrAM: A Compression-Aware Minimizer. CoRR abs/2207.14200 (2022) - 2021
- [j1]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22: 241:1-241:124 (2021) - [c2]Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh:
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. NeurIPS 2021: 8557-8570 - [i5]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR abs/2102.00554 (2021) - [i4]Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh:
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. CoRR abs/2106.12379 (2021) - [i3]Alexandra Peste, Dan Alistarh, Christoph H. Lampert:
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models. CoRR abs/2107.03860 (2021) - [i2]Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh:
How Well Do Sparse Imagenet Models Transfer? CoRR abs/2111.13445 (2021)
2010 – 2019
- 2018
- [i1]Septimia Sârbu, Riccardo Volpi, Alexandra Peste, Luigi Malagò:
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds. CoRR abs/1807.01889 (2018) - 2015
- [c1]Octavia-Maria Sulea, Liviu P. Dinu, Alexandra Peste:
Using NLP Specific Tools for Non-NLP Specific Tasks. A Web Security Application. ICONIP (4) 2015: 631-638
Coauthor Index
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