Eldar Kurtic

According to our database, Eldar Kurtic authored at least 17 papers between 2021 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark.

Eldar Kurtic, Torsten Hoefler, Dan Alistarh

CoRR, 2023

Sparse Fine-tuning for Inference Acceleration of Large Language Models.

Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh

CoRR, 2023

Accurate Neural Network Pruning Requires Rethinking Sparse Optimization.

Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh

CoRR, 2023

Error Feedback Can Accurately Compress Preconditioners.

Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Dan Alistarh

CoRR, 2023

Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression.

Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh

CoRR, 2023

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks.

Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

CoRR, 2023

ZipLM: Hardware-Aware Structured Pruning of Language Models.

Eldar Kurtic, Elias Frantar, Dan Alistarh

CoRR, 2023

CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models.

Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ZipLM: Inference-Aware Structured Pruning of Language Models.

Eldar Kurtic, Elias Frantar, Dan Alistarh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.

Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

Proceedings of the International Conference on Machine Learning, 2023

CrAM: A Compression-Aware Minimizer.

Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

oViT: An Accurate Second-Order Pruning Framework for Vision Transformers.

Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh

CoRR, 2022

GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods.

Eldar Kurtic, Dan Alistarh

CoRR, 2022

Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals.

Zlatan Ajanovic, Emina Alickovic, Aida Brankovic, Sead Delalic, Eldar Kurtic, Salem Malikic, Adnan Mehonic, Hamza Merzic, Kenan Sehic, Bahrudin Trbalic

CoRR, 2022

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.

Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization.

Elias Frantar, Eldar Kurtic, Dan Alistarh

CoRR, 2021

M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.

Elias Frantar, Eldar Kurtic, Dan Alistarh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021