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Advanced Topics in AI for Networking (Summer 2022): Difference between revisions
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Advanced Topics in AI for Networking (Summer 2022) (view source)
Revision as of 15:52, 30 April 2022
, 30 April 2022→List of Papers
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==List of Papers == | ==List of Papers == | ||
1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [https://ojs.aaai.org/index.php/AAAI/article/download/5379/5235] | 1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [https://ojs.aaai.org/index.php/AAAI/article/download/5379/5235] [L] | ||
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704] | 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] | ||
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] [ | 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] | ||
5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874] [ | 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] | ||
7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070] [ | 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] | ||
8. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434] | 8. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434] | ||