2019

  • A. Mathur, A. Isopoussu, F. Kawsar, N. Berthouze, and N. D. Lane, “Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems,” Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN 2019), 2019.
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  • M. Alizadeh, J. Fernández-Marqués, N. D. Lane, and Y. Gal, “An Empirical study of Binary Neural Networks’ Optimisation,” International Conference on Learning Representations, 2019.
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2018

  • J. Xu et al., “Embracing Spatial Awareness for Reliable WiFi-Based Indoor Location Systems,” in 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 2018, pp. 281–289.
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  • S. Bhattacharya et al., “Monitoring Daily Activities of Multiple Sclerosis Patients with Connected Health Devices,” in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018, pp. 666–669.
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  • A. Mathur, A. Isopoussu, F. Kawsar, R. Smith, N. D. Lane, and N. Berthouze, “On Robustness of Cloud Speech APIs: An Early Characterization,” in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018, pp. 1409–1413.
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  • V. W.-S. Tseng, S. Bhattacharya, J. F. Marqués, M. Alizadeh, C. Tong, and N. D. Lane, “Deterministic binary filters for convolutional neural networks,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018, pp. 2739–2747.
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  • J. Fernández-Marqués, V. W.-S. Tseng, S. Bhattachara, and N. D. Lane, “On-the-fly deterministic binary filters for memory efficient keyword spotting applications on embedded devices,” in Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning, 2018, pp. 13–18.
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  • J. Fernández-Marqués, V. W.-S. Tseng, S. Bhattachara, and N. D. Lane, “Deterministic binary filters for keyword spotting applications,” in Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, 2018, pp. 529–529.
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  • M. Alizadeh and N. D. Lane, “Using Pre-trained Full-Precision Models to Speed Up Training Binary Networks For Mobile Devices,” in Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, 2018, pp. 528–528.
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  • C. Tong et al., “Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data,” in Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, 2018, pp. 530–530.
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  • P. Veličković et al., “Cross-modal recurrent models for weight objective prediction from multimodal time-series data,” in Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2018, pp. 178–186.
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  • N. D. Lane and P. Warden, “The Deep (Learning) Transformation of Mobile and Embedded Computing,” Computer, vol. 51, no. 5, pp. 12–16, 2018.
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  • A. Mathur et al., “Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices,” in Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2018, pp. 200–211.
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  • V. Radu et al., “Multimodal deep learning for activity and context recognition,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4, p. 157, 2018.
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  • J. Fernández-Marqués, W.-S. T. Vincent, S. Bhattachara, and N. D. Lane, “BinaryCmd: Keyword Spotting with deterministic binary basis.” SysML, 2018.
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