Bringing ai to edge: from deep learning’s perspective

Cites (278)

Items 201–211 of 211

1. EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems
   Xiangzhong Luo, Di Liu, Hao Kong, Weichen Liu · 2020 · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD)
2. SBNet: Sparse Blocks Network for Fast Inference
   Mengye Ren, Andrei Pokrovsky, Bin Yang · 2018 · arXiv (Cornell University)
3. Densely Connected Search Space for More Flexible Neural Architecture Search
   Jiemin Fang, Yuzhu Sun, Qian Zhang · 2019 · arXiv (Cornell University)
4. Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
   Xin Li, Yiming Zhou, Zheng Yu Pan · 2019 · arXiv (Cornell University)
5. Achieving Resiliency and Behavior Assurance in Autonomous Navigation: An Industry Perspective
   Sanjoy Baruah, Peter Lee, Prakash Sarathy, Marilyn Wolf · 2020 · Proceedings of the IEEE
6. MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices
   Bo Chen, Golnaz Ghiasi, Hanxiao Liu · 2019 · arXiv (Cornell University)
7. Pruning from Scratch
   Wang, Yulong, Zhang, Xiaolu, Xie, Lingxi, Zhou, Jun, Su, Hang, Zhang, Bo, Hu, Xiaolin · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
8. S3NAS: Fast NPU-aware Neural Architecture Search Methodology
   Jaeseong Lee, Duseok Kang, Soonhoi Ha · 2020 · arXiv (Cornell University)
9. Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?
   Yunyang Xiong, Ronak Mehta, Vikas Singh · 2019 · arXiv (Cornell University)
10. Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
    Yawen Wu, Zhepeng Wang, Yiyu Shi · 2020 · arXiv (Cornell University)
11. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
    Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton, Toutanova, Kristina · 2018 · arXiv