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Elias Frantar
Elias Frantar
PhD Candidate, IST Austria
Verified email at ist.ac.at
Title
Cited by
Cited by
Year
GPTQ: Accurate post-training compression for generative pretrained transformers
E Frantar, S Ashkboos, T Hoefler, D Alistarh
arXiv preprint arXiv:2210.17323 1, 2022
346*2022
Sparsegpt: Massive language models can be accurately pruned in one-shot
E Frantar, D Alistarh
International Conference on Machine Learning, 10323-10337, 2023
1832023
Optimal brain compression: A framework for accurate post-training quantization and pruning
E Frantar, D Alistarh
Advances in Neural Information Processing Systems 35, 4475-4488, 2022
932022
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, ...
arXiv preprint arXiv:2203.07259, 2022
712022
Spqr: A sparse-quantized representation for near-lossless llm weight compression
T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ...
arXiv preprint arXiv:2306.03078, 2023
602023
M-fac: Efficient matrix-free approximations of second-order information
E Frantar, E Kurtic, D Alistarh
Advances in Neural Information Processing Systems 34, 14873-14886, 2021
412021
On the sample complexity of adversarial multi-source pac learning
N Konstantinov, E Frantar, D Alistarh, C Lampert
International Conference on Machine Learning, 5416-5425, 2020
252020
SPDY: Accurate pruning with speedup guarantees
E Frantar, D Alistarh
International Conference on Machine Learning, 6726-6743, 2022
192022
Ziplm: Hardware-aware structured pruning of language models
E Kurtic, E Frantar, D Alistarh
arXiv preprint arXiv:2302.04089 3 (7), 2023
162023
Towards end-to-end 4-bit inference on generative large language models
S Ashkboos, I Markov, E Frantar, T Zhong, X Wang, J Ren, T Hoefler, ...
arXiv preprint arXiv:2310.09259, 2023
62023
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
62023
Qmoe: Practical sub-1-bit compression of trillion-parameter models
E Frantar, D Alistarh
arXiv preprint arXiv:2310.16795, 2023
52023
Scaling laws for sparsely-connected foundation models
E Frantar, C Riquelme, N Houlsby, D Alistarh, U Evci
arXiv preprint arXiv:2309.08520, 2023
42023
L-greco: An efficient and general framework for layerwise-adaptive gradient compression
M Alimohammadi, I Markov, E Frantar, D Alistarh
arXiv preprint arXiv:2210.17357, 2022
42022
Extreme Compression of Large Language Models via Additive Quantization
V Egiazarian, A Panferov, D Kuznedelev, E Frantar, A Babenko, D Alistarh
arXiv preprint arXiv:2401.06118, 2024
32024
JaxPruner: A concise library for sparsity research
JH Lee, W Park, NE Mitchell, J Pilault, JSO Ceron, HB Kim, N Lee, ...
Conference on Parsimony and Learning, 515-528, 2024
32024
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
32023
ovit: An accurate second-order pruning framework for vision transformers
D Kuznedelev, E Kurtic, E Frantar, D Alistarh
22022
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
12024
QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models
T Pegolotti, E Frantar, D Alistarh, M Püschel
arXiv preprint arXiv:2307.03738, 2023
1*2023
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