ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2012.00596
  4. Cited By
NPAS: A Compiler-aware Framework of Unified Network Pruning and
  Architecture Search for Beyond Real-Time Mobile Acceleration
v1v2v3 (latest)

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

1 December 2020
Zhengang Li
Geng Yuan
Wei Niu
Pu Zhao
Yanyu Li
Yuxuan Cai
Xuan Shen
Zheng Zhan
Zhenglun Kong
Qing Jin
Zhiyu Chen
Sijia Liu
Kaiyuan Yang
Bin Ren
Yanzhi Wang
Xue Lin
    MQ
ArXiv (abs)PDFHTML

Papers citing "NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration"

8 / 58 papers shown
Title
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile
  Sensing Data Processing
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Shuochao Yao
Shaohan Hu
Yiran Zhao
Aston Zhang
Tarek Abdelzaher
HAIAI4TS
79
625
0
07 Nov 2016
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
478
5,381
0
05 Nov 2016
Dynamic Network Surgery for Efficient DNNs
Dynamic Network Surgery for Efficient DNNs
Yiwen Guo
Anbang Yao
Yurong Chen
84
1,059
0
16 Aug 2016
Learning Structured Sparsity in Deep Neural Networks
Learning Structured Sparsity in Deep Neural Networks
W. Wen
Chunpeng Wu
Yandan Wang
Yiran Chen
Hai Helen Li
187
2,341
0
12 Aug 2016
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large
  Datasets
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein
Stefan Falkner
Simon Bartels
Philipp Hennig
Frank Hutter
AI4CE
76
550
0
23 May 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
263
8,859
0
01 Oct 2015
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
313
6,700
0
08 Jun 2015
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
Hugo Larochelle
Ryan P. Adams
371
7,957
0
13 Jun 2012
Previous
12