Fetching the paper…
Reading the bibliography…
Network quantization is a dominant paradigm of model compression.
Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1995
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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.
Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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.
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.
Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
Earlier work this paper cites.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina · 2017
Earlier work this paper cites.
Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M. Roy · 2017
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
Earlier work this paper cites.
Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
Earlier work this paper cites.
SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
Earlier work this paper cites.
Loss-aware weight quantization of deep networks
Lu Hou and James T. Kwok · 2018
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, and Tom Goldstein · 2018
Earlier work this paper cites.
Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm
Zechun Liu, Baoyuan Wu, Wenhan Luo, Xin Yang, Wei Liu, and Kwang-Ting Cheng · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Earlier work this paper cites.
A bayesian perspective on generalization and stochastic gradient descent
Samuel L. Smith and Quoc V. Le · 2018
Earlier work this paper cites.
Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Earlier work this paper cites.
Towards effective low-bitwidth convolutional neural networks
Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, and Ian Reid · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Regularizing activation distribution for training binarized deep networks
Ruizhou Ding, Ting-Wu Chin, Zeye Liu, and Diana Marculescu · 2019
Cited alongside, same era.
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.
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
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
Closest in time.
Improving low-precision network quantization via bin regularization
Tiantian Han, Dong Li, Ji Liu, Lu Tian, and Yi Shan · 2021
Closest in time.
Distance-aware quantization
Dohyung Kim, Junghyup Lee, and Bumsub Ham · 2021
Closest in time.
I-bert: Integer-only bert quantization
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2021
Closest in time.
Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Jungmin Kwon, Jeongseop Kim, Hyun-Seok Park, and In Kwon Choi · 2021
Closest in time.
Network quantization with element-wise gradient scaling
Junghyup Lee, Dohyung Kim, and Bumsub Ham · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
Discovering low-precision networks close to full-precision networks for efficient inference
Jeffrey L McKinstry, Steven K Esser, Rathinakumar Appuswamy, Deepika Bablani, John V Arthur, Izzet B Yildiz, and Dharmendra S Modha · 2019
Cited alongside, same era.
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
Cited alongside, same era.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Cited alongside, same era.
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
Closest in time.
Fixed-point quantization for vision transformer
Zhexin Li, Peisong Wang, Zhiyuan Wang, and Jian Cheng · 2021
Closest in time.
How do adam and training strategies help bnns optimization
Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, and Kwang-Ting Cheng · 2021
Closest in time.
Post-training quantization for vision transformer
Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, and Wen Gao · 2021
Closest in time.
Learnable companding quantization for accurate low-bit neural networks
Kohei Yamamoto · 2021
Closest in time.
Regularizing neural networks via adversarial model perturbation
Yaowei Zheng, Richong Zhang, and Yongyi Mao · 2021
Closest in time.
When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2022
Closest in time.
Differentiable model compression via pseudo quantization noise
Alexandre Défossez, Yossi Adi, and Gabriel Synnaeve · 2022
Closest in time.
Efficient sharpness-aware minimization for improved training of neural networks
Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, and Vincent Tan · 2022
Closest in time.
Sharpness-aware training for free
Jiawei Du, Daquan Zhou, Jiashi Feng, Vincent YF Tan, and Joey Tianyi Zhou · 2022
Closest in time.
Fisher sam: Information geometry and sharpness aware minimisation
Minyoung Kim, Da Li, Shell X Hu, and Timothy Hospedales · 2022
Closest in time.
Q-vit: Fully differentiable quantization for vision transformer
Zhexin Li, Tong Yang, Peisong Wang, and Jian Cheng · 2022
Closest in time.
Towards efficient and scalable sharpness-aware minimization
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You · 2022
Closest in time.
Nonuniform-to-uniform quantization: Towards accurate quantization via generalized straight-through estimation
Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P Xing, and Zhiqiang Shen · 2022
Closest in time.
Train flat, then compress: Sharpness-aware minimization learns more compressible models
Clara Na, Sanket Vaibhav Mehta, and Emma Strubell · 2022
Closest in time.
Overcoming oscillations in quantization-aware training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, and Tijmen Blankevoort · 2022
Closest in time.
Bibert: Accurate fully binarized bert
Haotong Qin, Yifu Ding, Mingyuan Zhang, YAN Qinghua, Aishan Liu, Qingqing Dang, Ziwei Liu, and Xianglong Liu · 2022
Closest in time.
Learnable lookup table for neural network quantization
Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, and Yulan Guo · 2022
Closest in time.
Surrogate gap minimization improves sharpness-aware training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, James s Duncan, Ting Liu, et al · 2022
Closest in time.
An adaptive policy to employ sharpness-aware minimization
Weisen Jiang, Hansi Yang, Yu Zhang, and James Kwok · 2023
Closest in time.