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Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models.
A reduction of a graph to a canonical form and an algebra arising during this reduction
AA Leman and Boris Weisfeiler · 1968
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C. Mozer and Paul Smolensky · 1988
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Experimental determination of precision requirements for back-propagation training of artificial neural networks
Nelson Morgan et al · 1991
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Information, sensation, and perception
Kenneth H Norwich · 1993
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Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
Haim Avron and Sivan Toledo · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip H. S. Torr · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Value-aware quantization for training and inference of neural networks
Eunhyeok Park, Sungjoo Yoo, and Peter Vajda · 2018
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Value-aware quantization for training and inference of neural networks
Eunhyeok Park, Sungjoo Yoo, and Péter Vajda · 2018
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Faster gaze prediction with dense networks and fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
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Mixed precision quantization of convnets via differentiable neural architecture search
Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, and Kurt Keutzer · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
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Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
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Hawq: Hessian aware quantization of neural networks with mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
Cited alongside, same era.
Learned step size quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha · 2019
Cited alongside, same era.
Single path one-shot neural architecture search with uniform sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 2019
Cited alongside, same era.
Adabits: Neural network quantization with adaptive bit-widths
Qing Jin, Linjie Yang, and Zhenyu A. Liao · 2019
Cited alongside, same era.
Autoq: Automated kernel-wise neural network quantization
Qian Lou, Feng Guo, Lantao Liu, Minje Kim, and Lei Jiang · 2019
Cited alongside, same era.
Search what you want: Barrier panelty nas for mixed precision quantization
Haibao Yu, Qi Han, Jianbo Li, Jianping Shi, Guangliang Cheng, and Bin Fan · 2020
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Improving one-shot nas with shrinking-and-expanding supernet
Yiming Hu, Xingang Wang, Lujun Li, and Qingyi Gu · 2021
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Autoloss-zero: Searching loss functions from scratch for generic tasks
Hao Li, Tianwen Fu, Jifeng Dai, Hongsheng Li, Gao Huang, and Xizhou Zhu · 2021
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Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
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Zen-nas: A zero-shot nas for high-performance image recognition
Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin · 2021
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Cited alongside, same era.
Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
Rethinking differentiable search for mixed-precision neural networks
Zhaowei Cai and Nuno Vasconcelos · 2020
Cited alongside, same era.
One weight bitwidth to rule them all
Ting-Wu Chin, Pierce I-Jen Chuang, Vikas Chandra, and Diana Marculescu · 2020
Cited alongside, same era.
One weight bitwidth to rule them all
Ting-Wu Chin, I Pierce, Jen Chuang, Vikas Chandra, and Diana Marculescu · 2020
Cited alongside, same era.
Releq : A reinforcement learning approach for automatic deep quantization of neural networks
Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Amir Yazdanbakhsh, and Hadi Esmaeilzadeh · 2020
Cited alongside, same era.
Knowledge distillation: A survey, 2020
Jianping Gou, Baosheng Yu, Stephen John Maybank, and Dacheng Tao · 2020
Cited alongside, same era.
Exploring inter-channel correlation for diversity-preserved knowledge distillation
Li Liu, Qinwen Huang, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, and Xiao-Xue Liang · 2021
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Loss function discovery for object detection via convergence-simulation driven search
Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, and Zhenguo Li · 2021
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Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, and Mykola Pechenizkiy · 2021
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Epe-nas: Efficient performance estimation without training for neural architecture search
Vasco Lopes, Saeid Alirezazadeh, and Luís A. Alexandre · 2021
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Ompq: Orthogonal mixed precision quantization
Yuexiao Ma, Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Guannan Jiang, Wei Zhang, and Rongrong Ji · 2021
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Mae-det: Revisiting maximum entropy principle in zero-shot nas for efficient object detection
Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, and Rong Jin · 2021
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Hawq-v3: Dyadic neural network quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, et al · 2021
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Autoloss-gms: Searching generalized margin-based softmax loss function for person re-identification
Hongyang Gu, Jianmin Li, Guang zhi Fu, Chifong Wong, Xinghao Chen, and Jun Zhu · 2022
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Self-regulated feature learning via teacher-free feature distillation
Lujun Li · 2022
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Teacher-free distillation via regularizing intermediate representation
Lujun Li, Liang Shiuan-Ni, Ya Yang, and Zhe Jin · 2022
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Entropy-driven mixed-precision quantization for deep network design on iot devices
Zhenhong Sun, Ce Ge, Junyan Wang, Ming Lin, Hesen Chen, Hao Li, and Xiuyu Sun · 2022
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Diswot: Student architecture search for distillation without training
Peijie Dong, Lujun Li, and Zimian Wei · 2023
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Rd-nas: Enhancing one-shot supernet ranking ability via ranking distillation from zero-cost proxies
Peijie Dong, Xin Niu, Lujun Li, Zhiliang Tian, Xiaodong Wang, Zimian Wei, Hengyue Pan, and Dongsheng Li · 2023
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Automated knowledge distillation via monte carlo tree search
Lujun Li, Peijie Dong, Zimian Wei, and Ya Yang · 2023
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Norm: Knowledge distillation via n-to-one representation matching
Xiaolong Liu, Lujun Li, Chao Li, and Anbang Yao · 2023
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Catch-up distillation: You only need to train once for accelerating sampling
Shitong Shao, Xu Dai, Shouyi Yin, Lujun Li, Huanran Chen, and Yang Hu · 2023
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