Update README.md

htqin · Oct 16, 2023 · 0985648 · 0985648
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@@ -114,6 +114,8 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[TPAMI](https://ieeexplore.ieee.org/abstract/document/9735379)] Optimization-Based Post-Training Quantization With Bit-Split and Stitching
 - [[TPAMI](https://ieeexplore.ieee.org/abstract/document/10122994)] Single-path Bit Sharing for Automatic Loss-aware Model Compression
 - [[ICCV](https://openaccess.thecvf.com/content/ICCV2023/papers/Shang_Causal-DFQ_Causality_Guided_Data-Free_Network_Quantization_ICCV_2023_paper.pdf)] Causal-DFQ: Causality Guided Data-free Network Quantization [[code](https://github.com/42Shawn/Causal-DFQ)]
+- [[ICCV](https://arxiv.org/abs/2302.04304)] Q-Diffusion: Quantizing Diffusion Models [[code](https://github.com/Xiuyu-Li/q-diffusion)]
+- [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.pdf)] Post-training Quantization on Diffusion Models [[code](https://github.com/42Shawn/PTQ4DM)]
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Q-DETR_An_Efficient_Low-Bit_Quantized_Detection_Transformer_CVPR_2023_paper.pdf)] Q-DETR: An Efficient Low-Bit Quantized Detection Transformer [[code](https://github.com/SteveTsui/Q-DETR)]
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/html/Li_Hard_Sample_Matters_a_Lot_in_Zero-Shot_Quantization_CVPR_2023_paper.html)] Hard Sample Matters a Lot in Zero-Shot 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
@@ -128,8 +130,10 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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⭐]
+- [[EMNLP](https://arxiv.org/abs/2310.05079)] Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
 - [[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)]
 - [[TNNLS](https://ieeexplore.ieee.org/abstract/document/10227741)] Quantization via Distillation and Contrastive Learning.
+- [[HPCA](https://ieeexplore.ieee.org/document/9773213/)] Enabling High-Quality Uncertainty Quantification in a PIM Designed for Bayesian Neural Network
 - [[TIP](https://ieeexplore.ieee.org/abstract/document/10107717)] MBFQuant: A Multiplier-Bitwidth-Fixed, Mixed-Precision Quantization Method for Mobile CNN-Based Applications
 - [[TCSVT](https://ieeexplore.ieee.org/abstract/document/10132082)] Generative Data Free Model Quantization with Knowledge Matching for Classification [[code](https://github.com/ZSHsh98/KMDFQ)]
 - [[WACV](https://openaccess.thecvf.com/content/WACV2023/html/do_Nascimento_Hyperblock_Floating_Point_Generalised_Quantization_Scheme_for_Gradient_and_Inference_WACV_2023_paper.html)] Hyperblock Floating Point: Generalised Quantization Scheme for Gradient and Inference Computation
@@ -159,6 +163,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[arxiv](https://arxiv.org/abs/2307.09782)] ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
 - [[arxiv](https://arxiv.org/abs/2305.12356)] Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
 - [[arxiv](https://arxiv.org/abs/2304.01089)] RPTQ: Reorder-based Post-training Quantization for Large Language Models [[code](https://github.com/hahnyuan/RPTQ4LLM)]
+- [[arxiv](https://arxiv.org/abs/2304.09785)] Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
 - [[arxiv](https://arxiv.org/abs/2306.02272)] OWQ: Lessons learned from activation outliers for weight quantization in large language models
 - [[arxiv](https://arxiv.org/abs/2305.14152)] Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
 - [[arxiv](https://arxiv.org/abs/2310.00034)] PB-LLM: Partially Binarized Large Language Models
@@ -214,12 +219,12 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[arxiv](https://arxiv.org/pdf/2201.07703.pdf)] Q-ViT: Fully Differentiable Quantization for Vision Transformer [__`qnn`__]
 - [[arxiv](https://arxiv.org/pdf/2211.10438.pdf)] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models [__`qnn`__] [[code](https://github.com/mit-han-lab/smoothquant)] [150:star:]
 - [[arxiv](https://arxiv.org/pdf/2202.05048.pdf)] Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment [__`qnn`__]
-- [[IEEE Transactions on Geoscience and Remote Sensing](https://ieeexplore.ieee.org/abstract/document/9362309)] Accelerating Convolutional Neural Network-Based Hyperspectral Image Classification by Step Activation Quantization [__`qnn`__]
+- [[TGARS](https://ieeexplore.ieee.org/abstract/document/9362309)] Accelerating Convolutional Neural Network-Based Hyperspectral Image Classification by Step Activation Quantization [__`qnn`__]
 - [[arxiv](https://arxiv.org/pdf/2201.08442)] Neural network quantization with ai model efficiency toolkit (aimet).
 - [[IJNS](https://arxiv.org/pdf/2209.15317.pdf)] Convolutional Neural Networks Quantization with Attention.
 - [[ACM Trans. Des. Autom. Electron. Syst.](https://web.archive.org/web/20220722092230id_/https://dl.acm.org/doi/pdf/10.1145/3549535)] Structured Dynamic Precision for Deep Neural Networks uantization.
 - [[MICRO](https://ieeexplore.ieee.org/abstract/document/9923832)] ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization.
-- [[Empirical Software Engineering](https://link.springer.com/article/10.1007/s10664-022-10202-w)] DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment.
+- [[ESE](https://link.springer.com/article/10.1007/s10664-022-10202-w)] DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment.
 - [[TODAES](https://dl.acm.org/doi/10.1145/3498328)] Dynamic Quantization Range Control for Analog-in-Memory Neural Networks Acceleration.
 - [[CVPR](https://ieeexplore.ieee.org/document/9879477/)] BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning. [[torch](https://github.com/RU-System-Software-and-Security/BppAttack)]
 - [[IEEE Internet of Things Journal](https://ieeexplore.ieee.org/abstract/document/9915794)] FedQNN: A Computation–Communication-Efficient Federated Learning Framework for IoT With Low-Bitwidth Neural Network Quantization.