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AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles.

, , , , , , and . IEEE Trans. Mob. Comput., 20 (12): 3282-3297 (2021)

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Better accuracy with quantified privacy: representations learned via reconstructive adversarial network., , , and . CoRR, (2019)CentriForce: Multiple-Domain Adaptation for Domain-Invariant Speaker Representation Learning., , , and . IEEE Signal Process. Lett., (2022)AdaStreamLite: Environment-adaptive Streaming Speech Recognition on Mobile Devices., , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 7 (4): 187:1-187:29 (December 2023)Catch You as I Can: Indoor Localization via Ambient Sound Signature and Human Behavior., , , , and . IJDSN, (2013)SmartMeeting: An Novel Mobile Voice Meeting Minutes Generation and Analysis System., , , , , , , and . Mob. Networks Appl., 25 (2): 521-536 (2020)AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications., , , , and . CoRR, (2021)Air pollution source estimation profiling via mobile sensor networks., , , , and . CITS, page 1-5. IEEE, (2016)Random sketch learning for deep neural networks in edge computing., , , , , , , and . Nat. Comput. Sci., 1 (3): 221-228 (2021)CrowdBlueNet: Maximizing Crowd Data Collection Using Bluetooth Ad Hoc Networks., , , , , and . WASA, volume 9798 of Lecture Notes in Computer Science, page 356-366. Springer, (2016)CTFALite: Lightweight Channel-specific Temporal and Frequency Attention Mechanism for Enhancing the Speaker Embedding Extractor., , , and . INTERSPEECH, page 341-345. ISCA, (2022)