Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Davis, Andy"'
In this work, we begin to investigate the possibility of training a deep neural network on the task of binary code understanding. Specifically, the network would take, as input, features derived directly from binaries and output English descriptions
Externí odkaz:
http://arxiv.org/abs/2404.19631
This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall. The design of the proposed algorithm is motivate
Externí odkaz:
http://arxiv.org/abs/2206.14286
This paper presents an integrating decision support system to model food security in the UK. In ever-larger dynamic systems, such as the food system, it is increasingly difficult for decision-makers to effectively account for all the variables within
Externí odkaz:
http://arxiv.org/abs/2004.06764
Autor:
Lattner, Chris, Amini, Mehdi, Bondhugula, Uday, Cohen, Albert, Davis, Andy, Pienaar, Jacques, Riddle, River, Shpeisman, Tatiana, Vasilache, Nicolas, Zinenko, Oleksandr
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain speci
Externí odkaz:
http://arxiv.org/abs/2002.11054
Autor:
Olier, Ivan, Orhobor, Oghenejokpeme I., Dash, Tirtharaj, Davis, Andy M., Soldatova, Larisa N., Vanschoren, Joaquin, King, Ross D.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Dec 01. 118(49), 1-8.
Externí odkaz:
https://www.jstor.org/stable/27094145
Akademický článek
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Autor:
Yu, Yuan, Abadi, Martín, Barham, Paul, Brevdo, Eugene, Burrows, Mike, Davis, Andy, Dean, Jeff, Ghemawat, Sanjay, Harley, Tim, Hawkins, Peter, Isard, Michael, Kudlur, Manjunath, Monga, Rajat, Murray, Derek, Zheng, Xiaoqiang
Publikováno v:
EuroSys 2018: Thirteenth EuroSys Conference, April 23-26, 2018, Porto, Portugal. ACM, New York, NY, USA
Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditiona
Externí odkaz:
http://arxiv.org/abs/1805.01772
Autor:
Shazeer, Noam, Mirhoseini, Azalia, Maziarz, Krzysztof, Davis, Andy, Le, Quoc, Hinton, Geoffrey, Dean, Jeff
The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing mode
Externí odkaz:
http://arxiv.org/abs/1701.06538
Akademický článek
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Autor:
Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Irving, Geoffrey, Isard, Michael, Kudlur, Manjunath, Levenberg, Josh, Monga, Rajat, Moore, Sherry, Murray, Derek G., Steiner, Benoit, Tucker, Paul, Vasudevan, Vijay, Warden, Pete, Wicke, Martin, Yu, Yuan, Zheng, Xiaoqiang
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow
Externí odkaz:
http://arxiv.org/abs/1605.08695