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2020 – today
- 2024
[j1]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)- [c17]Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh:
QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models. EMNLP 2024: 3355-3371 - [c16]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR 2024 - [c15]Elias Frantar, Carlos Riquelme Ruiz, Neil Houlsby, Dan Alistarh, Utku Evci:
Scaling Laws for Sparsely-Connected Foundation Models. ICLR 2024 - [c14]Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh:
Extreme Compression of Large Language Models via Additive Quantization. ICML 2024 - [c13]Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh:
SPADE: Sparsity-Guided Debugging for Deep Neural Networks. ICML 2024 - [c12]Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh:
Error Feedback Can Accurately Compress Preconditioners. ICML 2024 - [c11]Elias Frantar, Dan Alistarh:
QMoE: Sub-1-Bit Compression of Trillion Parameter Models. MLSys 2024 - [c10]Ilia Markov, Kaveh Alim, Elias Frantar, Dan Alistarh:
L-GreCo: Layerwise-adaptive Gradient Compression For Efficient Data-parallel Deep Learning. MLSys 2024 - [i21]Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh:
Extreme Compression of Large Language Models via Additive Quantization. CoRR abs/2401.06118 (2024) - [i20]Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, Dan Alistarh:
MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. CoRR abs/2408.11743 (2024) - 2023
- [c9]Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh:
OPTQ: Accurate Quantization for Generative Pre-trained Transformers. ICLR 2023 - [c8]Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. ICML 2023: 10323-10337 - [c7]Eldar Kurtic, Elias Frantar, Dan Alistarh:
ZipLM: Inference-Aware Structured Pruning of Language Models. NeurIPS 2023 - [c6]Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh:
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models. NeurIPS 2023 - [i19]Elias Frantar, Dan Alistarh:
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot. CoRR abs/2301.00774 (2023) - [i18]Eldar Kurtic, Elias Frantar, Dan Alistarh:
ZipLM: Hardware-Aware Structured Pruning of Language Models. CoRR abs/2302.04089 (2023) - [i17]Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh:
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. CoRR abs/2303.14409 (2023) - [i16]Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci:
JaxPruner: A concise library for sparsity research. CoRR abs/2304.14082 (2023) - [i15]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR abs/2306.03078 (2023) - [i14]Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel:
QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. CoRR abs/2307.03738 (2023) - [i13]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) - [i12]Elias Frantar, Carlos Riquelme, Neil Houlsby, Dan Alistarh, Utku Evci:
Scaling Laws for Sparsely-Connected Foundation Models. CoRR abs/2309.08520 (2023) - [i11]Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin
, Dan Alistarh:
Sparse Fine-tuning for Inference Acceleration of Large Language Models. CoRR abs/2310.06927 (2023) - [i10]Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh:
Towards End-to-end 4-Bit Inference on Generative Large Language Models. CoRR abs/2310.09259 (2023) - [i9]Elias Frantar, Dan Alistarh:
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models. CoRR abs/2310.16795 (2023) - 2022
- [c5]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. EMNLP 2022: 4163-4181 - [c4]Elias Frantar, Dan Alistarh:
SPDY: Accurate Pruning with Speedup Guarantees. ICML 2022: 6726-6743 - [c3]Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. NeurIPS 2022 - [i8]Elias Frantar, Dan Alistarh:
SPDY: Accurate Pruning with Speedup Guarantees. CoRR abs/2201.13096 (2022) - [i7]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. CoRR abs/2203.07259 (2022) - [i6]Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. CoRR abs/2208.11580 (2022) - [i5]Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh:
oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. CoRR abs/2210.09223 (2022) - [i4]Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh:
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. CoRR abs/2210.17323 (2022) - [i3]Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar, Dan Alistarh:
L-GreCo: An Efficient and General Framework for Layerwise-Adaptive Gradient Compression. CoRR abs/2210.17357 (2022) - 2021
- [c2]Elias Frantar, Eldar Kurtic, Dan Alistarh:
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. NeurIPS 2021: 14873-14886 - [i2]Elias Frantar, Eldar Kurtic, Dan Alistarh:
Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. CoRR abs/2107.03356 (2021) - 2020
- [c1]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML 2020: 5416-5425 - [i1]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR abs/2002.10384 (2020)
Coauthor Index
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