Fetching the paper…
Reading the bibliography…
The Convolutional Neural Network (CNN) model, often used for image classification, requires significant training time to obtain high accuracy.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Tests for skewness, kurtosis, and normality for time series data
Jushan Bai and Serena Ng · 2005
Earlier work this paper cites.
Torch7: A matlab-like environment for machine learning
Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet · 2011
Earlier work this paper cites.
Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V Le, Mark Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Ng Andrew · 2012
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.
Building high-level features using large scale unsupervised learning
Quoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, and Andrew Y. Ng · 2012
Earlier work this paper cites.
Project adam: Building an efficient and scalable deep learning training system
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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.
One weird trick for parallelizing convolutional neural networks
Alex Krizhevsky · 2014
Earlier work this paper cites.
Scaling distributed machine learning with the parameter server
Mu Li, David G. Andersen, Jun Woo Park, Alexander J. Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J. Shekita, and Bor-Yiing Su · 2014
Earlier work this paper cites.
Overfeat:integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, and Yann LeCun · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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.
Petuum: A new platform for distributed machine learning on big data
Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu · 2015
Earlier work this paper cites.
A discriminative cnn video representation for event detection
Zhongwen Xu, Yi Yang, and Alex G Hauptmann · 2015
Earlier work this paper cites.
Exploiting image-trained cnn architectures for unconstrained video classification
Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi et al · 2016
Earlier work this paper cites.
Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server
Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, and Eric P. Xing · 2016
Cited alongside, same era.
Unsupervised learning of spoken language with visual context
David Harwath, Antonio Torralba, and James Glass · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
Cited alongside, same era.
Strads: a distributed framework for scheduled model parallel machine learning
Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A Gibson, and Eric P Xing · 2016
Cited alongside, same era.
Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, and Aixin Sun · 2017
Later among the works it cites.
Pipedream: Fast and efficient pipeline parallel dnn training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, and Phil Gibbons · 2018
Later among the works it cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, and Zhifeng Chen · 2018
Later among the works it cites.
Multi-tenant gpu clusters for deep learning workloads: Analysis and implications
Myeongjae Jeon, Shivaram Venkataraman, Junjie Qian, Amar Phanishayee, Wencong Xiao, and Fan Yang · 2018
Later among the works it cites.
Exploring hidden dimensions in parallelizing convolutional neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Augmenting supervised neural networks with unsupervised objectives for large-scale image classification
Yuting Zhang, Kibok Lee, and Honglak Lee · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Sparse communication for distributed gradient descent
Alham Fikri Aji and Kenneth Heafield · 2017
Cited alongside, same era.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Z. Li, Ryota Tomioka, and Milan Vojnovic · 2017
Cited alongside, same era.
Optimized broadcast for deep learning workloads on dense-gpu infiniband clusters: Mpi or nccl?
Ammar Ahmad Awan, Ching-Hsiang Chu, Hari Subramoni, and Dhabaleswar K. (DK) Panda · 2017
Cited alongside, same era.
S-caffe: Co-designing mpi runtimes and caffe for scalable deep learning on modern gpu clusters
Ammar Ahmad Awan, Khaled Hamidouche, Jahanzeb Maqbool Hashmi, and Dhabaleswar K. Panda · 2017
Cited alongside, same era.
Zhihao Jia, Sina Lin, Charles R. Qi, and Alex Aiken · 2018
Later among the works it cites.
Beyond data and model parallelism for deep neural networks
Zhihao Jia, Matei Zaharia, and Alex Aiken · 2018
Later among the works it cites.
Parameter hub: a rack-scale parameter server for distributed deep neural network training
Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, and Arvind Krishnamurthy · 2018
Later among the works it cites.
Optimus: an efficient dynamic resource scheduler for deep learning clusters
Yanghua Peng, Yixin Bao, Yangrui Chen, Chuan Wu, and Chuanxiong Guo · 2018
Later among the works it cites.
Horovod: Fast and easy distributed deep learning in tensorflow
A. Sergeev and M. Del Balso · 2018
Later among the works it cites.
Gradient sparsification for communication-efficient distributed optimization
Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang · 2018
Later among the works it cites.
Stanza: Distributed deep learning with small communication footprint
Xiaorui Wu, Hong Xu, Bo Li, and Yongqiang Xiong · 2018
Later among the works it cites.
Gandiva: Introspective cluster scheduling for deep learning
Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, and Lidong Zhou · 2018
Later among the works it cites.
Tiresias: A GPU cluster manager for distributed deep learning
Juncheng Gu, Chowdhury Chowdhury, Kang G. Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, and Chuanxiong Guo · 2019
Closest in time.
Normality testing - skewness and kurtosis
GoodData Help · 2019
Closest in time.
ILSVRC2017 dataset
ImageNet · 2019
Closest in time.
ImageNet dataset
ImageNet · 2019
Closest in time.
Tensorflow benchmarks
TensorFlow · 2019
Closest in time.
Distributed tensorflow
tensorflow.org · 2019
Closest in time.