Eldar Kurtic

According to our database1, Eldar Kurtic authored at least 24 papers between 2021 and 2024.

Collaborative distances:

Timeline

2021
2022
2023
2024
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10
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Links

On csauthors.net:

Bibliography

2024
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization.
Trans. Mach. Learn. Res., 2024

"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization.
CoRR, 2024

EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search.
CoRR, 2024

Panza: A Personalized Text Writing Assistant via Data Playback and Local Fine-Tuning.
CoRR, 2024

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.
CoRR, 2024

Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment.
CoRR, 2024

Error Feedback Can Accurately Compress Preconditioners.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark.
CoRR, 2023

Sparse Fine-tuning for Inference Acceleration of Large Language Models.
CoRR, 2023

Error Feedback Can Accurately Compress Preconditioners.
CoRR, 2023

Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression.
CoRR, 2023

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks.
CoRR, 2023

ZipLM: Hardware-Aware Structured Pruning of Language Models.
CoRR, 2023

CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models.
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.
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.
Proceedings of the International Conference on Machine Learning, 2023

CrAM: A Compression-Aware Minimizer.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
oViT: An Accurate Second-Order Pruning Framework for Vision Transformers.
CoRR, 2022

GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods.
CoRR, 2022

Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals.
CoRR, 2022

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
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.
CoRR, 2021

M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021


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