Update README.md

htqin · Nov 16, 2023 · aee1a8f · aee1a8f
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@@ -115,6 +115,9 @@ 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)]
 - [[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)]
@@ -136,11 +139,13 @@ 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
 - [[CVPR](https://arxiv.org/abs/2303.06424)] Regularized Vector Quantization for Tokenized Image Synthesis
 - [[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⭐]
+- [[ICLR](https://openreview.net/forum?id=3itjR9QxFw)] Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning 
 - [[ACL](https://arxiv.org/abs/2306.00014)] PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
 - [[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)]
+- [[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.
 - [[HPCA](https://ieeexplore.ieee.org/document/9773213/)] Enabling High-Quality Uncertainty Quantification in a PIM Designed for Bayesian Neural Network
@@ -162,12 +167,10 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[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. 
 - [[arxiv](https://arxiv.org/abs/2206.09557)] LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
 - [[arxiv](https://arxiv.org/abs/2306.03078)] SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression [[code](https://github.com/Vahe1994/SpQR)]
-- [[arxiv](https://arxiv.org/abs/2305.14314)] QLORA: Efficient Finetuning of Quantized LLMs [[code](https://github.com/artidoro/qlora)]
 - [[arxiv](https://arxiv.org/abs/2302.02390)] Quantized Distributed Training of Large Models with Convergence Guarantees
 - [[arxiv](https://arxiv.org/abs/2305.17888)] LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
 - [[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)]
@@ -385,6 +388,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,
 - [[CVPR](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.pdf)] BiDet: An Efficient Binarized Object Detector. [ **`qnn`** ] [[torch](https://github.com/ZiweiWangTHU/BiDet)] [112:star:] <!--citation 3-->
 - [[CVPR](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Fixed-Point_Back-Propagation_Training_CVPR_2020_paper.pdf)] Fixed-Point Back-Propagation Training. [[video](https://www.youtube.com/watch?v=nVRNygIQKI0)] [__`qnn`__]
 - [[CVPR](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w40/Yu_Low-Bit_Quantization_Needs_Good_Distribution_CVPRW_2020_paper.pdf)] Low-Bit Quantization Needs Good Distribution. [**`qnn`**] <!--citation 1-->
+- [[ICML](https://arxiv.org/abs/2004.09576)] LSQ+: Improving low-bit quantization through learnable offsets and better initialization
 - [[DATE](https://ieeexplore.ieee.org/document/9116220)] BNNsplit: Binarized Neural Networks for embedded distributed FPGA-based computing systems. [**`bnn`**] <!--citation 1-->
 - [[DATE](https://arxiv.org/abs/1912.04050)] PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones. [**`bnn`**] [**`hardware`**]
 - [[DATE](https://ieeexplore.ieee.org/abstract/document/9116308)] OrthrusPE: Runtime Reconfigurable Processing Elements for Binary Neural Networks. [**`bnn`**] <!--citation 2-->