540
edits
Advanced Topics in AI for Networking (Summer 2022): Difference between revisions
Advanced Topics in AI for Networking (Summer 2022) (view source)
Revision as of 15:53, 30 April 2022
, 30 April 2022→List of Papers
| Line 37: | Line 37: | ||
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704][A] | 2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704][A] | ||
3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448] | 3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448][A] | ||
4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882] [B] | 4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882] [B] | ||
| Line 43: | Line 43: | ||
5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874] [B] | 5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874] [B] | ||
6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] | 6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [L] | ||
7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070] [B] | 7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070] [B] | ||
| Line 53: | Line 53: | ||
10. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | 10. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | ||
11. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | 11. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] [L] | ||
12. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927] | 12. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927][A] | ||
13. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928] | 13. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928] | ||