Update README.md

README.md

@@ -110,19 +110,20 @@ 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)]
-- [[ICML](https://arxiv.org/abs/2306.00317)] FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization [[code](https://openreview.net/attachment?id=-tYCaP0phY_&name=supplementary_material)]
-- [[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)]
-- [[ICML](https://icml.cc/virtual/2023/poster/23915)] The case for 4-bit precision: k-bit Inference Scaling Laws
+- [[NeurIPS](https://arxiv.org/abs/2305.10299)] Binarized Spectral Compressive Imaging [[code](https://github.com/caiyuanhao1998/BiSCI)]
 - [[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
+- [[ICML](https://arxiv.org/pdf/2301.11233.pdf)] BiBench: Benchmarking and Analyzing Network Binarization [**`bnn`**] [[code](https://github.com/htqin/BiBench)]
+- [[ICML](https://arxiv.org/abs/2306.00317)] FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization [[code](https://openreview.net/attachment?id=-tYCaP0phY_&name=supplementary_material)]
+- [[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)]
+- [[ICML](https://icml.cc/virtual/2023/poster/23915)] The case for 4-bit precision: k-bit Inference Scaling Laws
 - [[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)]