Update README.md

README.md

@@ -119,6 +119,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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)]
+- [[ICCV](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_QD-BEV__Quantization-aware_View-guided_Distillation_for_Multi-view_3D_Object_Detection_ICCV_2023_paper.html)] QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
 - [[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
@@ -139,6 +140,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[ACL](https://aclanthology.org/2023.findings-acl.15/)] Boost Transformer-based Language Models with GPU-Friendly Sparsity and Quantization
 - [[EMNLP](https://arxiv.org/abs/2310.05079)] Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
 - [[EMNLP](https://arxiv.org/abs/2310.13315)] Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models
+- [[EMNLP](https://arxiv.org/abs/2310.16836)] LLM-FP4: 4-Bit Floating-Point Quantized Transformers [[code](https://github.com/nbasyl/LLM-FP4)]
 - [[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
@@ -159,6 +161,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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/abs/2304.01089)] RPTQ: Reorder-based Post-training Quantization for Large Language Models. [[code](https://github.com/hahnyuan/RPTQ4LLM)]
 - [[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/2304.09785)] Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric. [__`ptq`__]
 - [[arxiv](https://arxiv.org/abs/2305.12356)] Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models. 
@@ -171,17 +174,17 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[arxiv](https://arxiv.org/abs/2306.11987)] Training Transformers with 4-bit Integers [[code](https://github.com/xijiu9/Train_Transformers_with_INT4)]
 - [[arxiv](https://arxiv.org/abs/2305.11186)] Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
 - [[arxiv](https://arxiv.org/abs/2305.10657)] PTQD: Accurate Post-Training Quantization for Diffusion 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/2305.18723)] Towards Accurate Data-free Quantization for Diffusion Models
 - [[arxiv](https://arxiv.org/abs/2306.02316)] Temporal Dynamic Quantization for Diffusion Models
 - [[arxiv](https://arxiv.org/abs/2306.08162)] INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
-- [[arxiv](https://arxiv.org/abs/2307.03712)] INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers [[code](https://github.com/lightmatter-ai/INT-FP-QSim)]
 - [[arxiv](https://arxiv.org/abs/2306.07629)] SqueezeLLM: Dense-and-Sparse Quantization [[code](https://github.com/SqueezeAILab/SqueezeLLM)]
 - [[arxiv](https://arxiv.org/abs/2306.12929)] Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
 - [[arxiv](https://arxiv.org/abs/2306.13515)] Binary domain generalization for sparsifying binary neural networks. [__`bnn`__]
+- [[arxiv](https://arxiv.org/abs/2307.03712)] INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers [[code](https://github.com/lightmatter-ai/INT-FP-QSim)]
 - [[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/2304.01089)] RPTQ: Reorder-based Post-training Quantization for Large Language Models. [[code](https://github.com/hahnyuan/RPTQ4LLM)]
 - [[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/2307.08072)] Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
 - [[arxiv](https://arxiv.org/abs/2307.13304)] QuIP: 2-Bit Quantization of Large Language Models With Guarantees. [[code](https://github.com/jerry-chee/QuIP)]
 - [[arxiv](https://arxiv.org/abs/2308.05600)] NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
 - [[arxiv](https://arxiv.org/abs/2308.06744)] Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
@@ -207,6 +210,10 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[arxiv](https://arxiv.org/abs/2310.08659)] LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models [[code](https://github.com/yxli2123/LoftQ)]
 - [[arxiv](https://arxiv.org/abs/2310.10944)] TEQ: Trainable Equivalent Transformation for Quantization of LLMs [[code](https://github.com/intel/neural-compressor)]
 - [[arxiv](https://arxiv.org/abs/2310.11453)] BitNet: Scaling 1-bit Transformers for Large Language Models [[code](https://github.com/kyegomez/BitNet)]
+- [[arxiv](https://arxiv.org/abs/2310.18313)] FP8-LM: Training FP8 Large Language Models [[code](https://github.com/Azure/MS-AMP)]
+- [[arxiv](https://arxiv.org/abs/2310.19102)] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving [[code](https://github.com/efeslab/Atom)]
+- [[arxiv](https://arxiv.org/abs/2311.01305)] AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models
+- [[arxiv](https://arxiv.org/abs/2311.01792)] AFPQ: Asymmetric Floating Point Quantization for LLMs [[code](https://github.com/zhangsichengsjtu/AFPQ)]
 
 ### 2022