Update README.md

README.md

@@ -111,8 +111,12 @@ 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)]
-- [[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
+- [[TPAMI](https://ieeexplore.ieee.org/abstract/document/9735379)] Optimization-Based Post-Training Quantization With Bit-Split and Stitching
+- [[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)]
+- [[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
+- [[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://openaccess.thecvf.com/content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html)] PD-Quant: Post-Training Quantization Based on Prediction Difference Metric [[code](https://github.com/hustvl/PD-Quant)]
 - [[CVPR](https://arxiv.org/abs/2303.06869)] Adaptive Data-Free Quantization
 - [[CVPR](https://arxiv.org/pdf/2211.16056.pdf)] NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
 - [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.pdf)] Boost Vision Transformer with GPU-Friendly Sparsity and Quantization
@@ -123,6 +127,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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/html/Yvinec_SPIQ_Data-Free_Per-Channel_Static_Input_Quantization_WACV_2023_paper.html)] SPIQ: Data-Free Per-Channel Static Input Quantization
 - [[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.
@@ -152,11 +157,13 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 ### 2022
 
 - [[ECCV](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136710416.pdf)] Weight Fixing Networks. [__`qnn`__] [[code]](https://github.com/subiawaud/Weight_Fix_Networks)
+- [[ECCV](https://link.springer.com/chapter/10.1007/978-3-031-20071-7_37)] Neuromorphic Data Augmentation for Training Spiking Neural Networks. [__`snn`__] [[code]](https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN)
 - [[IJCV](https://arxiv.org/abs/2109.12338)] Distribution-sensitive Information Retention for Accurate Binary Neural Network. [__`bnn`__]
 - [[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`**]
 - [[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)] 
 - [[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`__]
@@ -232,6 +239,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[LNAI](https://link.springer.com/chapter/10.1007/978-3-031-04083-2_14)] ECQ$^x$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.
 - [[TCCN](https://ieeexplore.ieee.org/abstract/document/9703679)] Low-Bitwidth Convolutional Neural Networks for Wireless Interference Identification.
 - [[ASE](https://dl.acm.org/doi/abs/10.1145/3551349.3556916)] QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks.
+- [[ICPR](https://ieeexplore.ieee.org/abstract/document/9956237)] Layer-Wise Data-Free CNN Compression.
 - [[IJCNN](https://ieeexplore.ieee.org/abstract/document/9892671)] Accuracy Evaluation of Transposed Convolution-Based Quantized Neural Networks.
 - [[NeurIPS]](https://arxiv.org/abs/2209.13325) Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models. [[code](https://github.com/wimh966/outlier_suppression)]
 - [[ACL](https://aclanthology.org/2022.acl-long.331)] Compression of Generative Pre-trained Language Models via Quantization
@@ -246,6 +254,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[ICML](https://proceedings.mlr.press/v139/kim21d.html)] I-BERT: Integer-only BERT Quantization. [**`qnn`**]
 - [[ICML](https://proceedings.mlr.press/v139/zhang21r.html)] Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution. [**`qnn`**]
 - [[ICML](https://proceedings.mlr.press/v139/fu21d.html)] Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. [**`qnn`**]
+- [[ICCV](https://openaccess.thecvf.com/content/ICCV2021/html/Li_MixMix_All_You_Need_for_Data-Free_Compression_Are_Feature_and_ICCV_2021_paper.html)] MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing.
 - [[CVPR](http:https://openaccess.thecvf.com/content/CVPR2021/html/Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper.html)] S2-bnn: Bridging the gap between self-supervised real and 1-bit neural networks via guided distribution calibration [**`bnn`**] [[code](https://github.com/szq0214/S2-BNN)] [52⭐]
 - [[CVPR](https://arxiv.org/abs/2103.01049)] Diversifying Sample Generation for Accurate Data-Free Quantization. [__`qnn`__]
 - [[ACM MM](https://dl.acm.org/doi/10.1145/3474085.3475224)] VQMG: Hierarchical Vector Quantised and Multi-hops Graph Reasoning for Explicit Representation Learning. [__`other`__]