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Continuous Learning for Lightweight Machine Learning Inference at the Edge. MIT, USA, (2023)Gemino: Practical and Robust Neural Compression for Video Conferencing., , , , , , , and . CoRR, (2022)Gemino: Practical and Robust Neural Compression for Video Conferencing., , , , , , , and . NSDI, page 569-590. USENIX Association, (2024)RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics., , , , , , , and . NSDI, page 917-932. USENIX Association, (2023)Temporal Dynamics of Connectivity and Epidemic Properties of Growing Networks., and . CoRR, (2015)SiP-ML: high-bandwidth optical network interconnects for machine learning training., , , , , , , , and . SIGCOMM, page 657-675. ACM, (2021)End-to-end transport for video QoE fairness., , , , , and . SIGCOMM, page 408-423. ACM, (2019)Exploiting Channel Locality for Adaptive Massive MIMO Signal Detection., , , and . ICASSP, page 8565-8568. IEEE, (2020)Efficient Video Compression via Content-Adaptive Super-Resolution., , and . ICCV, page 4501-4510. IEEE, (2021)Real-Time Video Inference on Edge Devices via Adaptive Model Streaming., , , and . ICCV, page 4552-4562. IEEE, (2021)