Update README.md

README.md

@@ -115,9 +115,14 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[ICML](https://arxiv.org/abs/2301.12017)] Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases [[code](https://github.com/microsoft/DeepSpeed)]
 - [[ICML](https://icml.cc/virtual/2023/28295)] GPT-Zip: Deep Compression of Finetuned Large Language Models
 - [[ICML](https://arxiv.org/abs/2307.03738)] QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models [[code](https://github.com/IST-DASLab/QIGen)]
-- [[NeurIPS](https://arxiv.org/abs/2305.14314)] QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution [[code](https://github.com/htqin/QuantSR)]
-- [[NeurIPS](https://arxiv.org/abs/2305.14314)] BiMatting: Efficient Video Matting via Binarization [[code](https://github.com/htqin/BiMatting)]
-- [[NeurIPS](https://arxiv.org/abs/2305.14314)] QLORA: Efficient Finetuning of Quantized LLMs [[code](https://github.com/artidoro/qlora)]
+- [[ICML](https://icml.cc/virtual/2023/poster/23915)] The case for 4-bit precision: k-bit Inference Scaling Laws
+- [[NeurIPS](https://dev.neurips.cc/virtual/2023/poster/72890)] QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution [[code](https://github.com/htqin/QuantSR)]
+- [[NeurIPS](https://neurips.cc/virtual/2023/poster/71287)] BiMatting: Efficient Video Matting via Binarization [[code](https://github.com/htqin/BiMatting)]
+- [[NeurIPS](https://nips.cc/virtual/2023/oral/73855)] QLORA: Efficient Finetuning of Quantized LLMs [[code](https://github.com/artidoro/qlora)]
+- [[NeurIPS](https://neurips.cc/virtual/2023/poster/70279)] Q-DM: An Efficient Low-bit Quantized Diffusion Model
+- [[NeurIPS](https://neurips.cc/virtual/2023/poster/71314)] PTQD: Accurate Post-Training Quantization for Diffusion Models [[code](https://github.com/ziplab/PTQD)]
+- [[NeurIPS](https://nips.cc/virtual/2023/poster/72396)] Temporal Dynamic Quantization for Diffusion Models
+- [[NeurIPS](https://nips.cc/virtual/2023/poster/72931)] Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
 - [[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)]
@@ -145,6 +150,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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)]
+- [[EMNLP](https://arxiv.org/abs/2310.11237)] Watermarking LLMs with Weight Quantization [[code](https://github.com/Twilight92z/Quantize-Watermark)]
 - [[EMNLP](https://arxiv.org/abs/2304.09145)] Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
 - [[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.
@@ -176,10 +182,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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/2306.11987)] 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/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
@@ -508,6 +511,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[NeurIPS](https://www.emc2-ai.org/assets/docs/neurips-19/emc2-neurips19-paper-31.pdf)] [44:fire:] Q8BERT: Quantized 8Bit BERT. [**`qnn`**] [**`nlp`**]
 - [[NeurIPS](https://www.emc2-ai.org/assets/docs/neurips-19/emc2-neurips19-paper-36.pdf)] Fully Quantized Transformer for Improved Translation. [**`qnn`**] [**`nlp`**]
 - [[NeurIPS](https://openreview.net/pdf?id=rJgB34rx8r)] Normalization Helps Training of Quantized LSTM. [**`qnn`**] [**`bnn`**]
+- [[NeurIPS](https://arxiv.org/abs/1902.03538)] Model Compression with Adversarial Robustness: A Unified Optimization Framework. [**`qnn`**]
 - [[RoEduNet](https://ieeexplore.ieee.org/document/8909493/)] PXNOR: Perturbative Binary Neural Network. [**`bnn`**] [[code](https://github.com/Apfelin/PXNOR)]
 - [[SiPS](https://arxiv.org/abs/1909.01688)] Knowledge distillation for optimization of quantized deep neural networks. [**`qnn`**]
 - [[TMM](https://arxiv.org/abs/1708.05127)] [45:fire:] Deep Binary Reconstruction for Cross-Modal Hashing. [**`bnn`**]