| The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models E Kurtic, D Campos, T Nguyen, E Frantar, M Kurtz, B Fineran, M Goin, ... EMNLP 2022, 2022 | 135 | 2022 |
| M-FAC: Efficient Matrix-Free Approximations of Second-Order Information E Frantar, E Kurtic, D Alistarh NeurIPS 2021, 2021 | 63 | 2021 |
| ZipLM: Inference-Aware Structured Pruning of Language Models E Kurtić, E Frantar, D Alistarh Advances in Neural Information Processing Systems 36, 2024 | 34 | 2024 |
| ZipLM: Hardware-Aware Structured Pruning of Language Models E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2302.04089, 2023 | 25 | 2023 |
| Sparse Finetuning for Inference Acceleration of Large Language Models E Kurtic, D Kuznedelev, E Frantar, M Goin, D Alistarh arXiv preprint arXiv:2310.06927, 2023 | 17 | 2023 |
| GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods E Kurtic, D Alistarh arXiv preprint arXiv:2210.06384, 2022 | 16 | 2022 |
| CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models D Kuznedelev, E Kurtić, E Frantar, D Alistarh Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| CrAM: A Compression-Aware Minimizer A Peste, A Vladu, E Kurtic, CH Lampert, D Alistarh ICLR 2023, 2022 | 10 | 2022 |
| Accurate Neural Network Pruning Requires Rethinking Sparse Optimization D Kuznedelev, E Kurtic, E Iofinova, E Frantar, A Peste, D Alistarh arXiv preprint arXiv:2308.02060, 2023 | 8 | 2023 |
| SparseProp: efficient sparse backpropagation for faster training of neural networks at the edge M Nikdan, T Pegolotti, E Iofinova, E Kurtic, D Alistarh International Conference on Machine Learning, 26215-26227, 2023 | 8 | 2023 |
| Microadam: Accurate adaptive optimization with low space overhead and provable convergence, 2024 IV Modoranu, M Safaryan, G Malinovsky, E Kurtic, T Robert, P Richtarik, ... URL https://arxiv. org/abs/2405.15593, 0 | 7 | |
| Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models E Kurtic, A Moeini, D Alistarh EMNLP 2024, 2024 | 6 | 2024 |
| Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment A Agarwalla, A Gupta, A Marques, S Pandit, M Goin, E Kurtic, K Leong, ... arXiv preprint arXiv:2405.03594, 2024 | 6 | 2024 |
| Error Feedback Can Accurately Compress Preconditioners IV Modoranu, A Kalinov, E Kurtic, D Alistarh arXiv preprint arXiv:2306.06098, 2023 | 5 | 2023 |
| SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks M Nikdan, T Pegolotti, E Iofinova, E Kurtic, D Alistarh ICML 2023, 2023 | 4 | 2023 |
| oViT: An Accurate Second-Order Pruning Framework for Vision Transformers D Kuznedelev, E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2210.09223, 2022 | 3 | 2022 |
| MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence IV Modoranu, M Safaryan, G Malinovsky, E Kurtic, T Robert, P Richtarik, ... arXiv preprint arXiv:2405.15593, 2024 | 2 | 2024 |
| How to Prune Your Language Model: Recovering Accuracy on the “Sparsity May Cry” Benchmark E Kurtic, T Hoefler, D Alistarh Conference on Parsimony and Learning, 542-553, 2024 | 2 | 2024 |
| " Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization E Kurtic, A Marques, S Pandit, M Kurtz, D Alistarh arXiv preprint arXiv:2411.02355, 2024 | 1 | 2024 |
| EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search O Sieberling, D Kuznedelev, E Kurtic, D Alistarh arXiv preprint arXiv:2410.14649, 2024 | 1 | 2024 |