Purine: A bi-graph based deep learning framework

Dryad: distributed data-parallel programs from sequential building blocks

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly · 2007

Earlier work this paper cites.

Pig latin: a not-so-foreign language for data processing

Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, and Andrew Tomkins · 2008

Earlier work this paper cites.

Theano: a CPU and GPU math expression compiler

James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio · 2010

Earlier work this paper cites.

Graphlab: A new framework for parallel machine learning

Original

Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph M Hellerstein · 2010

Earlier work this paper cites.

Large scale distributed deep networks

Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012

Cited alongside, same era.

Building high-level features using large scale unsupervised learning

Quoc V Le · 2013

Cited alongside, same era.

Project adam: Building an efficient and scalable deep learning training system

Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014

Cited alongside, same era.

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

Cited alongside, same era.

One weird trick for parallelizing convolutional neural networks

Original

Alex Krizhevsky · 2014

Closest in time.

1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns

Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014

Closest in time.

Sequence to sequence learning with neural networks

Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014

Closest in time.

Going deeper with convolutions

Original

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014

Closest in time.