Frantar, Elias

18 publications

TMLR 2025 TACO 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

TMLR 2024 Accurate Neural Network Pruning Requires Rethinking Sparse Optimization Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh

ICML 2024 Error Feedback Can Accurately Compress Preconditioners Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh

ICML 2024 Extreme Compression of Large Language Models via Additive Quantization Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh

CPAL 2024 Jaxpruner: A Concise Library for Sparsity Research Joo Hyung Lee, Wonpyo Park, Nicole Elyse Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J.C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci

ICML 2024 SPADE: Sparsity-Guided Debugging for Deep Neural Networks Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh

ICLR 2024 Scaling Laws for Sparsely-Connected Foundation Models Elias Frantar, Carlos Riquelme Ruiz, Neil Houlsby, Dan Alistarh, Utku Evci

ICLR 2024 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression Tim Dettmers, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

NeurIPS 2023 CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models Denis Kuznedelev, Eldar Kurtić, Elias Frantar, Dan Alistarh

ICMLW 2023 Generating Efficient Kernels for Quantized Inference on Large Language Models Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel

ICLR 2023 OPTQ: Accurate Quantization for Generative Pre-Trained Transformers Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh

ICML 2023 SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot Elias Frantar, Dan Alistarh

NeurIPS 2023 ZipLM: Inference-Aware Structured Pruning of Language Models Eldar Kurtić, Elias Frantar, Dan Alistarh

ICMLW 2023 ZipLM: Inference-Aware Structured Pruning of Language Models Eldar Kurtic, Elias Frantar, Dan Alistarh

NeurIPS 2022 Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning Elias Frantar, Dan Alistarh

ICML 2022 SPDY: Accurate Pruning with Speedup Guarantees Elias Frantar, Dan Alistarh

NeurIPS 2021 M-FAC: Efficient Matrix-Free Approximations of Second-Order Information Elias Frantar, Eldar Kurtic, Dan Alistarh

ICML 2020 On the Sample Complexity of Adversarial Multi-Source PAC Learning Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert