2020

  • D. J. Beutel, T. Topal, A. Mathur, X. Qiu, T. Parcollet, and N. D. Lane, “Flower: A Friendly Federated Learning Research Framework,” arXiv preprint arXiv:2007.14390, 2020.
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  • J. Fernandez-Marques, P. Whatmough, A. Mundy, and M. Mattina, “Searching for Winograd-aware Quantized Networks,” in Conference on Machine Learning and Systems (MLSys), 2020.
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  • M. Alizadeh, A. Behboodi, M. van Baalen, C. Louizos, T. Blankevoort, and M. Welling, “Gradient \(\ell_1\)Regularization for Quantization Robustness,” in International Conference on Learning Representations, 2020.
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  • R. Lee et al., “Journey Towards Tiny Perceptual Super-Resolution,” European Conference on Computer Vision, 2020.
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  • J. van Amersfoort, M. Alizadeh, S. Farquhar, N. Lane, and Y. Gal, “Single Shot Structured Pruning Before Training,” arXiv preprint arXiv:2007.00389, 2020.
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  • S. Bhattacharya, D. Manousakas, A. G. C. P. Ramos, S. I. Venieris, N. D. Lane, and C. Mascolo, “Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, no. 2, pp. 1–24, 2020.
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  • H. Kwon et al., “IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition,” arXiv preprint arXiv:2006.05675, 2020.
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  • Y. Gao, T. Parcollet, and N. Lane, “Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition,” arXiv preprint arXiv:2005.09310, 2020.
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  • X. Qiu, T. Parcollet, M. Ravanelli, N. Lane, and M. Morchid, “Quaternion Neural Networks for Multi-channel Distant Speech Recognition,” Interspeech, 2020.
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  • R. Vipperla, S. Ishtiaq, R. Li, S. Bhattacharya, I. Leontiadis, and N. D. Lane, “LEARNING TO LISTEN... ON-DEVICE: Present and future perspectives of on-device ASR,” GetMobile: Mobile Computing and Communications, vol. 23, no. 4, pp. 5–9, 2020.
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  • M. Zhang et al., “Deep Learning in the Era of Edge Computing: Challenges and Opportunities,” Fog Computing: Theory and Practice, pp. 67–78, 2020.
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  • A. Mathur, F. Kawsar, N. Berthouze, and N. D. Lane, “Libri-Adapt: a New Speech Dataset for Unsupervised Domain Adaptation,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 7439–7443.
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  • V. Kothari, E. Liberis, and N. D. Lane, “The Final Frontier: Deep Learning in Space,” in Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, 2020, pp. 45–49.
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  • C. Tong, S. A. Tailor, and N. D. Lane, “Are Accelerometers for Activity Recognition a Dead-end?,” in Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, 2020, pp. 39–44.
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  • M. S. Abdelfattah, L. Dudziak, T. C. P. Chau, R. Lee, H. Kim, and N. D. Lane, “Codesign-NAS: Automatic FPGA/CNN Codesign Using Neural Architecture Search,” in FPGA ’20: The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2020, p. 315.
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  • M. S. Abdelfattah, Ł. Dudziak, T. Chau, R. Lee, H. Kim, and N. D. Lane, “Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator,” Design Automation Conference, 2020.
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  • N. Berthouze et al., “Emopain challenge 2020: Multimodal pain evaluation from facial and bodily expressions,” arXiv preprint arXiv:2001.07739, 2020.
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  • S. A. Osia et al., “A hybrid deep learning architecture for privacy-preserving mobile analytics,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4505–4518, 2020.
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2019

  • A. Mathur, A. Isopoussu, F. Kawsar, N. B. Berthouze, and N. D. Lane, “Flexadapt: Flexible cycle-consistent adversarial domain adaptation,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019, pp. 896–901.
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  • R. Lee, S. I. Venieris, L. Dudziak, S. Bhattacharya, and N. D. Lane, “Mobisr: Efficient on-device super-resolution through heterogeneous mobile processors,” in The 25th Annual International Conference on Mobile Computing and Networking, 2019, pp. 1–16.
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  • E. Liberis and N. D. Lane, “Neural networks on microcontrollers: saving memory at inference via operator reordering,” arXiv preprint arXiv:1910.05110, 2019.
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  • C. Tong, M. Craner, M. Vegreville, and N. D. Lane, “Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connnected Wellness Devices,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 3, pp. 1–19, 2019.
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  • A. Mathur, A. Isopoussu, N. Berthouze, N. D. Lane, and F. Kawsar, “Unsupervised domain adaptation for robust sensory systems,” in Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 2019, pp. 505–509.
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  • C. Wang, T. A. Olugbade, A. Mathur, A. C. De C. Williams, N. D. Lane, and N. Bianchi-Berthouze, “Recurrent network based automatic detection of chronic pain protective behavior using mocap and semg data,” in Proceedings of the 23rd International Symposium on Wearable Computers, 2019, pp. 225–230.
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  • C. Wang, M. Peng, T. A. Olugbade, N. D. Lane, A. C. de C. Williams, and N. Bianchi-Berthouze, “Learning temporal and bodily attention in protective movement behavior detection,” in 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 2019, pp. 324–330.
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  • Ł. Dudziak, M. S. Abdelfattah, R. Vipperla, S. Laskaridis, and N. D. Lane, “Shrinkml: End-to-end asr model compression using reinforcement learning,” arXiv preprint arXiv:1907.03540, 2019.
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  • M. Almeida, S. Laskaridis, I. Leontiadis, S. I. Venieris, and N. D. Lane, “EmBench: Quantifying performance variations of deep neural networks across modern commodity devices,” in The 3rd International Workshop on Deep Learning for Mobile Systems and Applications, 2019, pp. 1–6.
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  • 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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  • A. Mathur, F. Kawsar, N. Berthouze, and N. D. Lane, “A Vision for Adaptive and Generalizable Audio-Sensing Systems,” in Proceedings of the Fourth International Workshop on Social Sensing, 2019, pp. 43–43.
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  • C. Wang, T. A. Olugbade, A. Mathur, A. C. D. C. Williams, N. D. Lane, and N. Bianchi-Berthouze, “Automatic detection of protective behavior in chronic pain physical rehabilitation: A recurrent neural network approach,” arXiv preprint arXiv:1902.08990, 2019.
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  • A. Mathur, A. Isopoussu, F. Kawsar, R. Smith, N. Berthouze, and N. D. Lane, “Towards the Design and Evaluation of Robust Audio-Sensing Systems,” in Human Activity Sensing, Springer, 2019, pp. 47–57.
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  • M. Alizadeh, J. Fernandez-Marques, 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. Fernandez-Marques, 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. Fernandez-Marques, 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. Fernandez-Marques, 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. Fernandez-Marques, W.-S. T. Vincent, S. Bhattachara, and N. D. Lane, “BinaryCmd: Keyword Spotting with deterministic binary basis.” SysML, 2018.
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