Yu, M., Lin, Z., Narra, K., Li, S., Li, Y., Kim, N.S., Schwing, A, Annavaram, M., and Avestimehr, AS. GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. Retrieved from https://par.nsf.gov/biblio/10108636. Neural Information Processing - Letters and Reviews .
Yu, M., Lin, Z., Narra, K., Li, S., Li, Y., Kim, N.S., Schwing, A, Annavaram, M., & Avestimehr, AS. GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. Neural Information Processing - Letters and Reviews, (). Retrieved from https://par.nsf.gov/biblio/10108636.
Yu, M., Lin, Z., Narra, K., Li, S., Li, Y., Kim, N.S., Schwing, A, Annavaram, M., and Avestimehr, AS.
"GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training". Neural Information Processing - Letters and Reviews (). Country unknown/Code not available. https://par.nsf.gov/biblio/10108636.
@article{osti_10108636,
place = {Country unknown/Code not available},
title = {GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training},
url = {https://par.nsf.gov/biblio/10108636},
abstractNote = {},
journal = {Neural Information Processing - Letters and Reviews},
author = {Yu, M. and Lin, Z. and Narra, K. and Li, S. and Li, Y. and Kim, N.S. and Schwing, A and Annavaram, M. and Avestimehr, AS},
}
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