Update README.md

README.md

@@ -111,7 +111,6 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 ### 2023
 
 - [[ICML](https://arxiv.org/pdf/2301.11233.pdf)] BiBench: Benchmarking and Analyzing Network Binarization [**`bnn`**] [[code](https://github.com/htqin/BiBench)]
-- [[ISCA](https://dl.acm.org/doi/abs/10.1145/3579371.3589038)] OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_Toward_Accurate_Post-Training_Quantization_for_Image_Super_Resolution_CVPR_2023_paper.pdf)]Toward Accurate Post-Training Quantization for Image Super Resolution
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/papers/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.pdf)]One-Shot Model for Mixed-Precision Quantization
 - [[CVPR](https://arxiv.org/abs/2303.06869)] Adaptive Data-Free Quantization
@@ -122,19 +121,20 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[CVPR](https://arxiv.org/pdf/2303.11906.pdf)] Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
 - [[CVPR](https://ipl.dgist.ac.kr/ABCD_cvpr23.pdf)] ABCD : Arbitrary Bitwise Coefficient for De-quantization
 - [[CVPR](https://arxiv.org/abs/2212.04780)] GENIE: Show Me the Data for Quantization
+- [[ICLR](https://arxiv.org/abs/2210.17323)] GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers [[code](https://github.com/IST-DASLab/gptq)] [721⭐]
 - [[TNNLS](https://ieeexplore.ieee.org/document/10049753/)] BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance. [__`bnn`__] [[code](https://github.com/htqin/BiFSMNv2)]
 - [[WACV](https://openaccess.thecvf.com/content/WACV2023/papers/Pham_Collaborative_Multi-Teacher_Knowledge_Distillation_for_Learning_Low_Bit-Width_Deep_Neural_WACV_2023_paper.pdf)] Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks.
 - [[PR](https://www.sciencedirect.com/science/article/pii/S0031320323001632)] Bayesian asymmetric quantized neural networks.
 - [[Cognitive Neurodynamics](https://link.springer.com/article/10.1007/s11571-022-09927-7)] Pruning and quantization algorithm with applications in memristor-based convolutional neural network.
 - [[MMM](https://link.springer.com/chapter/10.1007/978-3-031-27077-2_8)] Binary Neural Network for Video Action Recognition. [__`bnn`__]
+- [[ISCA](https://dl.acm.org/doi/abs/10.1145/3579371.3589038)] OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
 - [[arxiv](https://arxiv.org/pdf/2211.10438.pdf)] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. [[code](https://github.com/mit-han-lab/smoothquant)] [387⭐]
 - [[arxiv](https://arxiv.org/pdf/2201.11113.pdf)] Post-training Quantization for Neural Networks with Provable Guarantees.
 - [[arxiv](https://arxiv.org/abs/2303.12557)] Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction.
 - [[arxiv](https://arxiv.org/abs/2303.12270)] EBSR: Enhanced Binary Neural Network for Image Super-Resolution. [__`bnn`__]
 - [[arxiv](https://arxiv.org/abs/2303.15493)] Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis. [__`bnn`__]
 - [[arxiv](https://arxiv.org/pdf/2304.09145.pdf)] Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.
 - [[arxiv](https://arxiv.org/abs/2206.09557)] LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
-- [[ICLR](https://arxiv.org/abs/2210.17323)] GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers [[code](https://github.com/IST-DASLab/gptq)] [721⭐]
 - [[arxiv](https://arxiv.org/abs/2306.00978)] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration [[code](https://github.com/mit-han-lab/llm-awq)]
 - [[arxiv](https://arxiv.org/abs/2306.03078)] SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression [[code](https://github.com/Vahe1994/SpQR)]
 - [[arxiv](https://arxiv.org/abs/2305.14314)] QLORA: Efficient Finetuning of Quantized LLMs [[code](https://github.com/artidoro/qlora)]