Fetching the paper…
Reading the bibliography…
Recently low-bit (e.g., 8-bit) network quantization has been extensively studied to accelerate the inference.
High performance convolutional neural networks for document processing
Patrice Simard Kumar Chellapilla, Sidd Puri · 2006
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
R-fcn: Object detection via region-based fully convolutional networks
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
cnn-benchmarks
jcjohnson · 2016
Earlier work this paper cites.
Performance analysis of gpu-based convolutional neural networks
Xiaqing Li, Guangyan Zhang, H Howie Huang, Zhufan Wang, and Weimin Zheng · 2016
Earlier work this paper cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Earlier work this paper cites.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Cited alongside, same era.
Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
Cited alongside, same era.
Flexpoint: An adaptive numerical format for efficient training of deep neural networks
Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, and Naveen Rao · 2017
Cited alongside, same era.
Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 2017
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Later among the works it cites.
Haq: Hardware-aware automated quantization
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2018
Later among the works it cites.
Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
Later among the works it cites.
Two-step quantization for low-bit neural networks
Peisong Wang, Qinghao Hu, Yifan Zhang, Chunjie Zhang, Yang Liu, and Jian Cheng · 2018
Later among the works it cites.
Training and inference with integers in deep neural networks
Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar · 2017
Cited alongside, same era.
Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
Asit Mishra and Debbie Marr · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
Cited alongside, same era.
Scalable methods for 8-bit training of neural networks, 2018
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
Post-training 4-bit quantization of convolution networks for rapid-deployment
Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
Cited alongside, same era.
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Later among the works it cites.
An instruction set architecture for machine learning
Yunji Chen, Huiying Lan, Zidong Du, Shaoli Liu, Jinhua Tao, Dong Han, Tao Luo, Qi Guo, Ling Li, Yuan Xie, et al · 2019
Closest in time.
Regularizing activation distribution for training binarized deep networks
Ruizhou Ding, Ting-Wu Chin, Zeye Liu, and Diana Marculescu · 2019
Closest in time.
Learned step size quantization
Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha · 2019
Closest in time.
Differentiable soft quantization: Bridging full-precision and low-bit neural networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
Closest in time.
Simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation
Zhezhi He and Deliang Fan · 2019
Closest in time.
Analysis of quantized models
Lu Hou, Ruiliang Zhang, and James T. Kwok · 2019
Closest in time.
Learning to quantize deep networks by optimizing quantization intervals with task loss
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi · 2019
Closest in time.
Fully quantized network for object detection
Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, and Rui Fan · 2019
Closest in time.
Adaptive gradient methods with dynamic bound of learning rate
Liangchen Luo, Yuanhao Xiong, Yan Liu, and Xu Sun · 2019
Closest in time.
Per-tensor fixed-point quantization of the back-propagation algorithm
Charbel Sakr and Naresh Shanbhag · 2019
Closest in time.
Quantization networks
Jiwei Yang, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, and Xian-sheng Hua · 2019
Closest in time.
Training high-performance and large-scale deep neural networks with full 8-bit integers, 2019
Yukuan Yang, Shuang Wu, Lei Deng, Tianyi Yan, Yuan Xie, and Guoqi Li · 2019
Closest in time.
Blended coarse gradient descent for full quantization of deep neural networks
Penghang Yin, Shuai Zhang, Jiancheng Lyu, Stanley Osher, Yingyong Qi, and Jack Xin · 2019
Closest in time.