Fetching the paper…
Reading the bibliography…
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection.
Optimal brain damage
Yann LeCun, John S. Denker, and Sara A. Solla · 1990
Earlier work this paper cites.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Feature hashing for large scale multitask learning
Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, and Alexander J. Smola · 2009
Earlier work this paper cites.
Improving the speed of neural networks on cpus
Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao · 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.
Do deep nets really need to be deep?
Lei Jimmy Ba and Rich Caurana · 2013
Earlier work this paper cites.
Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando de Freitas · 2013
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and application to data-parallel distributed training of speech dnns
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 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.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Resiliency of deep neural networks under quantization
Wonyong Sung, Sungho Shin, and Kyuyeon Hwang · 2015
Cited alongside, same era.
FINN: A framework for fast, scalable binarized neural network inference
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, and Kees A. Vissers · 2016
Later among the works it cites.
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
G. Urban, K. J. Geras, S. Ebrahimi Kahou, O. Aslan, S. Wang, R. Caruana, A. Mohamed, M. Philipose, and M. Richardson · 2016
Later among the works it cites.
Accelerating deep convolutional networks using low-precision and sparsity
Ganesh Venkatesh, Eriko Nurvitadhi, and Debbie Marr · 2016
Later among the works it cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An analysis of deep neural network models for practical applications
Alfredo Canziani, Adam Paszke, and Eugenio Culurciello · 2016
Cited alongside, same era.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Matthieu Courbariaux and Yoshua Bengio · 2016
Cited alongside, same era.
EIE: efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Fengfu Li and Bin Liu · 2016
Cited alongside, same era.
Convolutional neural networks using logarithmic data representation
Daisuke Miyashita, Edward H. Lee, and Boris Murmann · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Later among the works it cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, and Yuheng Zou · 2016
Later among the works it cites.
Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2016
Later among the works it cites.
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
A. Ashok, N. Rhinehart, F. Beainy, and K. M. Kitani · 2017
Closest in time.
Ternary Neural Networks with Fine-Grained Quantization
N. Mellempudi, A. Kundu, D. Mudigere, D. Das, B. Kaul, and P. Dubey · 2017
Closest in time.
WRPN: Wide Reduced-Precision Networks
A. Mishra, E. Nurvitadhi, J. J Cook, and D. Marr · 2017
Closest in time.
SCNN: an accelerator for compressed-sparse convolutional neural networks
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel S. Emer, Stephen W. Keckler, and William J. Dally · 2017
Closest in time.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
Closest in time.
Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
Closest in time.