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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
- N. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, P. T. P. Tang
- Computer ScienceICLR
- 15 September 2016
TLDR
Deep Learning Recommendation Model for Personalization and Recommendation Systems
- M. Naumov, D. Mudigere, M. Smelyanskiy
- Computer ScienceArXiv
- 31 May 2019
TLDR
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
- V. Lee, Changkyu Kim, P. Dubey
- Computer ScienceISCA
- 19 June 2010
TLDR
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
- Kim M. Hazelwood, Sarah Bird, Xiaodong Wang
- Computer ScienceIEEE International Symposium on High Performance…
- 1 February 2018
TLDR
Glow: Graph Lowering Compiler Techniques for Neural Networks
- Nadav Rotem, Jordan Fix, M. Smelyanskiy
- Computer ScienceArXiv
- 2 May 2018
TLDR
The Architectural Implications of Facebook's DNN-Based Personalized Recommendation
- Udit Gupta, Xiaodong Wang, Xuan Zhang
- Computer ScienceIEEE International Symposium on High Performance…
- 6 June 2019
TLDR
Efficient sparse matrix-vector multiplication on x86-based many-core processors
- Xing Liu, M. Smelyanskiy, Edmond Chow, P. Dubey
- Computer ScienceICS '13
- 10 June 2013
TLDR
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
- Liu Ke, Udit Gupta, Xiaodong Wang
- Computer ScienceACM/IEEE 47th Annual International Symposium on…
- 30 December 2019
TLDR
qHiPSTER: The Quantum High Performance Software Testing Environment
- M. Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik
- Computer Science, PhysicsArXiv
- 26 January 2016
We present qHiPSTER, the Quantum High Performance Software Testing Environment. qHiPSTER is a distributed high-performance implementation of a quantum simulator on a classical computer, that can…
Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications
- Yen-kuang Chen, J. Chhugani, M. Smelyanskiy
- Computer ScienceProceedings of the IEEE
- 15 April 2008
TLDR
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