Launchpad: A Programming Model for Distributed Machine Learning Research

Asynchronous methods for deep reinforcement learning

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937

Earlier work this paper cites.

Implementing remote procedure calls

Birrell, A. D. and Nelson, B. J. (1984) · 1984

Earlier work this paper cites.

Using MPI: portable parallel programming with the message-passing interface

Gropp, W., Gropp, W. D., Lusk, A. D. F. E. E., Lusk, E., and Skjellum, A. (1999) · 1999

Earlier work this paper cites.

Acme: A research framework for distributed reinforcement learning

Original

Hoffman, M., Shahriari, B., Aslanides, J., Barth-Maron, G., Behbahani, F., Norman, T., Abdolmaleki, A., Cassirer, A., Yang, F., Baumli, K., Henderson, S., Novikov, A., Colmenarejo, S. G., Cabi, S., Gulcehre, C., Paine, T. L., Cowie, A., Wang, Z., Piot, B., and de Freitas, N. (2020) · 2006

Earlier work this paper cites.

Mapreduce: simplified data processing on large clusters

Dean, J. and Ghemawat, S. (2008) · 2008

Earlier work this paper cites.

A simple modification in cma-es achieving linear time and space complexity

Ros, R. and Hansen, N. (2008) · 2008

Earlier work this paper cites.

Natural evolution strategies

Wierstra, D., Schaul, T., Peters, J., and Schmidhuber, J. (2008) · 2008

Earlier work this paper cites.

Scalable inference in latent variable models

Ahmed, A., Aly, M., Gonzalez, J., Narayanamurthy, S., and Smola, A. J. (2012) · 2012

Cited alongside, same era.

Imagenet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012

Cited alongside, same era.

Caffe: Convolutional architecture for fast feature embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014) · 2014

Cited alongside, same era.

Scaling distributed machine learning with the parameter server

Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y. (2014) · 2014

Cited alongside, same era.

TensorFlow: Large-scale machine learning on heterogeneous systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015) · 2015

Automatic differentiation in PyTorch

Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017

Later among the works it cites.

JAX: composable transformations of Python+NumPy programs

Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q. (2018) · 2018

Later among the works it cites.

Large scale gan training for high fidelity natural image synthesis

Original

Brock, A., Donahue, J., and Simonyan, K. (2018) · 2018

Later among the works it cites.

IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures

Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K. (2018) · 2018

Later among the works it cites.

Rlgraph: Flexible computation graphs for deep reinforcement learning

Original

Schaarschmidt, M., Mika, S., Fricke, K., and Yoneki, E. (2018) · 2018