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README.md

@@ -106,20 +106,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 
 **Statistics**: :fire: highly cited | :star: code is available and star > 50
 
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-### 2023 
-
-- [[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. 
-- [[arxiv](https://arxiv.org/pdf/2211.10438.pdf)] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. 
-- [[arxiv](https://arxiv.org/pdf/2201.11113.pdf)] Post-training Quantization for Neural Networks with Provable Guarantees. 
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 ### 2023
 
@@ -168,13 +155,14 @@ Deep Neural Networks.
 - [[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/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/2307.09782)] 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/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
+- [[arxiv](https://arxiv.org/abs/2306.13515)] Binary domain generalization for sparsifying binary neural networks. [__`bnn`__]
+- [[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/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. [[code](https://github.com/hahnyuan/PB-LLM)]
+- [[arxiv](https://arxiv.org/abs/2304.09785)] Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric. [__`ptq`__]
 
 ### 2022
 
@@ -184,14 +172,9 @@ Deep Neural Networks.
 - [[ICML](https://proceedings.mlr.press/v162/huang22h.html)] SDQ: Stochastic Differentiable Quantization with Mixed Precision [**`qnn`**]
 - [[ICML](https://proceedings.mlr.press/v162/dong22a.html)] Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks [**`qnn`**] [**`hardware`**]
 - [[ICML](https://proceedings.mlr.press/v162/liu22v.html)] GACT: Activation Compressed Training for Generic Network Architectures [**`qnn`**]
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 - [[ICLR](https://openreview.net/forum?id=5xEgrl_5FAJ)] BiBERT: Accurate Fully Binarized BERT. [**`bnn`**][code](https://github.com/htqin/BiBERT)]
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2022/html/Guo_RecDis-SNN_Rectifying_Membrane_Potential_Distribution_for_Directly_Training_Spiking_Neural_CVPR_2022_paper.html)] RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks. [**`snn`**]
 - [[CVPR](https://arxiv.org/abs/2203.17008)] It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher. [**`qnn`**] [[code](https://github.com/iamkanghyunchoi/ait)] 
-=======
-- [[ICLR](https://openreview.net/forum?id=5xEgrl_5FAJ)] BiBERT: Accurate Fully Binarized BERT. [**`bnn`**] [[code](https://github.com/htqin/BiBERT)]
-- [[ICLR](https://openreview.net/forum?id=tcbBPnfwxS)] OPTQ: Accurate Quantization for Generative Pre-trained Transformers. [[code](https://github.com/IST-DASLab/gptq)]
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 - [[CVPR](https://arxiv.org/abs/2111.14826)] Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. [**`qnn`**] [[code](https://github.com/liuzechun/Nonuniform-to-Uniform-Quantization)] [59⭐]
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learnable_Lookup_Table_for_Neural_Network_Quantization_CVPR_2022_paper.pdf)] Learnable Lookup Table for Neural Network Quantization. [__`qnn`__]
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2022/papers/Jeon_Mr.BiQ_Post-Training_Non-Uniform_Quantization_Based_on_Minimizing_the_Reconstruction_Error_CVPR_2022_paper.pdf)] Mr.BiQ: Post-Training Non-Uniform Quantization based on Minimizing the Reconstruction Error. [__`qnn`__]