Fetching the paper…
Reading the bibliography…
Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 1998
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 et al · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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.
Adaptive dropout for training deep neural networks
Jimmy Ba and Brendan Frey · 2013
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
Earlier work this paper cites.
Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev · 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.
Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
Daniel Soudry, Itay Hubara, and Ron Meir · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Zhiyong Cheng, Daniel Soudry, Zexi Mao, and Zhenzhong Lan · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Earlier work this paper cites.
Statistical learning with sparsity: the lasso and generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Earlier work this paper cites.
Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
Cited alongside, same era.
An efficient hardware accelerator for sparse convolutional neural networks on fpgas
Liqiang Lu, Jiaming Xie, Ruirui Huang, Jiansong Zhang, Wei Lin, and Yun Liang · 2019
Later among the works it cites.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Ari Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Later among the works it cites.
Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Later among the works it cites.
Tide: A general toolbox for identifying object detection errors
Daniel Bolya, Sean Foley, James Hays, and Judy Hoffman · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
Cited alongside, same era.
Benchmarking and error diagnosis in multi-instance pose estimation
Matteo Ruggero Ronchi and Pietro Perona · 2017
Cited alongside, same era.
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Cited alongside, same era.
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
Cited alongside, same era.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, and Zhifeng Chen · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Cited alongside, same era.
Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
Cited alongside, same era.
Successfully applying the stabilized lottery ticket hypothesis to the transformer architecture
C Brix, P Bahar, and H Ney · 2020
Closest in time.
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, and Zhangyang Wang · 2020
Closest in time.
The lottery ticket hypothesis for pre-trained bert networks
T Chen, J Frankle, S Chang, S Liu, Y Zhang, Z Wang, and M Carbin · 2020
Closest in time.
Fast sparse convnets
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan · 2020
Closest in time.
Stabilizing the lottery ticket hypothesis
J Frankle, G K Dziugaite, D M Roy, and M Carbin · 2020
Closest in time.
How many winning tickets are there in one dnn?
Kathrin Grosse and Michael Backes · 2020
Closest in time.
Proving the lottery ticket hypothesis: Pruning is all you need
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, and Ohad Shamir · 2020
Closest in time.
Sparse transfer learning via winning lottery tickets
R Mehta · 2020
Closest in time.
Rajiv Movva and Jason Y. Zhao · 2020
Closest in time.
Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
Closest in time.
An experimental analysis of model compression techniques for object detection
Andrey Salvi and Rodrigo Barros · 2020
Closest in time.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Closest in time.
Drawing early-bird tickets: Toward more efficient training of deep networks
H You, C Li, P Xu, Y Fu, Y Wang, X Chen, R G Baraniuk, Z Wang, and Y Lin · 2020
Closest in time.
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
H Yu, S Edunov S, Y Tian Y, and A S Morcos · 2020
Closest in time.