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Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations.

, , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1 (3): 50:1-50:19 (2017)

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ZOE: A Cloud-less Dialog-enabled Continuous Sensing Wearable Exploiting Heterogeneous Computation., , , and . MobiSys, page 273-286. ACM, (2015)Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations., , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1 (3): 50:1-50:19 (2017)Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning., , , , , , , , , and 13 other author(s). CoRR, (2021)Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading., , , and . MobiSys, page 306-318. ACM, (2017)An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices., , , , and . IoT-App@SenSys, page 7-12. ACM, (2015)Evaluating Multimodal Interactive Agents., , , , , , , , , and 5 other author(s). CoRR, (2022)Imitating Interactive Intelligence., , , , , , , , , and 14 other author(s). CoRR, (2020)Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX., , , , and . IPSN, page 61:1-61:2. IEEE, (2016)Demo: Accelerated Deep Learning Inference for Embedded and Wearable Devices using DeepX., , , , and . MobiSys (Companion Volume), page 109. ACM, (2016)LEO: scheduling sensor inference algorithms across heterogeneous mobile processors and network resources., , , and . MobiCom, page 320-333. ACM, (2016)