This template makes it easy for you to manage papers.
- Add paper information by
./add_paper_info.sh
or./add_paper_info.sh <name>
- Run
./refresh_readme.sh
sparsegpt.prototxt
paper {
title: "SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot."
abbr: "SparseGPT"
url: "https://arxiv.org/pdf/2301.00774.pdf"
authors: "Elias Frantar"
authors: "Dan Alistarh"
institutions: "IST Austria"
institutions: "Neural Magic"
}
pub {
where: "arXiv"
year: 2023
}
code {
type: "Pytorch"
url: "https://github.com/IST-DASLab/sparsegpt"
}
note {
url: "SparseGPT.md"
}
keyword {
words: "sparsity"
}
Distributed
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Decentralized_FM | Decentralized_FM_alpha | Github | 2022 | Pytorch |
Quantization
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch |
Sparse/Pruning
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Deep Compression | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding | ICLR | 2016 | |||
1 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 | |||
2 | abbr | DFPC: Data flow driven pruning of coupled channels without data | ICLR | 2023 | |||
3 | abbr | Holistic Adversarially Robust Pruning | ICLR | 2023 | |||
4 | MVUE | Minimum Variance Unbiased N:M Sparsity for the Neural Gradients | ICLR | 2023 | |||
5 | abbr | Pruning Deep Neural Networks from a Sparsity Perspective | ICLR | 2023 | |||
6 | abbr | Rethinking Graph Lottery Tickets: Graph Sparsity Matters | ICLR | 2023 | |||
7 | SMC | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! | ICLR | 2023 | SMC-Bench | ||
8 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
2016
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Deep Compression | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding | ICLR | 2016 |
2021
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 |
2022
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Decentralized_FM | Decentralized_FM_alpha | Github | 2022 | Pytorch |
2023
Github
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Decentralized_FM | Decentralized_FM_alpha | Github | 2022 | Pytorch |
ICCV workshop
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 |
ICLR
arXiv
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch | ||
1 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
DS3Lab
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Decentralized_FM | Decentralized_FM_alpha | Github | 2022 | Pytorch |
Eindhoven University of Technology
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SMC | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! | ICLR | 2023 | SMC-Bench |
Habana Labs
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | MVUE | Minimum Variance Unbiased N:M Sparsity for the Neural Gradients | ICLR | 2023 |
Houmo AI
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch |
IST Austria
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
Intel Corporation
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 |
Neural Magic
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
Stanford University
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Deep Compression | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding | ICLR | 2016 |
Tencent AI Lab
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch |
University of Texas at Austin
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SMC | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! | ICLR | 2023 | SMC-Bench |
inst1
inst2
Bingzhe Wu
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch |
Brian Chmiel
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | MVUE | Minimum Variance Unbiased N:M Sparsity for the Neural Gradients | ICLR | 2023 |
Dan Alistarh
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
Daniel Soudry
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | MVUE | Minimum Variance Unbiased N:M Sparsity for the Neural Gradients | ICLR | 2023 |
Elias Frantar
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SparseGPT | SparseGPT: Massive Language Models Can be Accurately Pruned in one-shot. | arXiv | 2023 | Pytorch | note |
Ivan Lazarevich
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 |
Jue Wang
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Decentralized_FM | Decentralized_FM_alpha | Github | 2022 | Pytorch |
Name1
Name2
Nikita Malinin
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | OpenVINO | Post-training deep neural network pruning via layer-wise calibration | ICCV workshop | 2021 |
Shiwei Liu
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SMC | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! | ICLR | 2023 | SMC-Bench |
Song Han
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | Deep Compression | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding | ICLR | 2016 |
Zhangyang Wang
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | SMC | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! | ICLR | 2023 | SMC-Bench |
Zhihang Yuan
meta | title | publication | year | code | note | cover | |
---|---|---|---|---|---|---|---|
0 | RPTQ | RPTQ: Reorder-based Post-training Quantization for Large Language Models | arXiv | 2023 | PyTorch |