Last Update: May 6, 2017.

I know, I know, no one really needs one more long lists of selected papers about ML. But I need this list to be somewhere so that I can drop the link with a message: “Say who needs ML on mobile one more time.” 🤓

During the last year, my work was revolving around mobile information security, so the list is obviously biased in this direction. As usually, order is random.

Applications: Computer Vision

Andrey Ignatov, Nikolay Kobyshev, Kenneth Vanhoey, Radu Timofte, Luc Van Gool. (2017). DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks. arXiv

Joachim Dehais, Marios Anthimopoulos, Sergey Shevchik, Stavroula Mougiakakou. (2017). Two-view 3D Reconstruction for Food Volume Estimation. arXiv

Michał Nowicki, Jan Wietrzykowski, Piotr Skrzypczyński. (2017). Real-Time Visual Place Recognition for Personal Localization on a Mobile Device. arXiv

Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra Bhandarkar, Wojciech Matusik, Antonio Torralba. (2016). Eye Tracking for Everyone. arXiv

Applications: Mobile Sensors

del Rosario, M. B., Redmond, S. J., & Lovell, N. H. (2015). Tracking the evolution of smartphone sensing for monitoring human movement. NCBI

Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2016). DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing. arXiv

Shoaib, M., Bosch, S., Incel, O., Scholten, H., Havinga, P. (2015). A Survey of Online Activity Recognition Using Mobile Phones. MDPI

Habib, M. A., Mohktar, M. S., Kamaruzzaman, S. B., Lim, K. S., Pin, T. M., Ibrahim, F. (2014). Smartphone-based solutions for fall detection and prevention: challenges and open issues. NCBI

Lane, N. D., & Georgiev, P. (2015). Can Deep Learning Revolutionize Mobile Sensing? PDF

Masaya Inoue, Sozo Inoue, Takeshi Nishida. (2017). Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput. arXiv

Dogukan Erenel, Haluk O. Bingol. (2016). An Accelerometer Based Calculator for Visually Impaired People Using Mobile Devices. arXiv

Applications: NLP

Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann et al. (2016). Smart Reply: Automated Response Suggestion for Email. arXiv

Applications: Mobile Infosec

Marquardt, P., Verma, A., Carter, H., & Traynor, P. (2011). (sp) iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers. PDF

Narain, S., Sanatinia, A., & Noubir, G. (2014). Single-stroke language-agnostic keylogging using stereo-microphones and domain specific machine learning. PDF

Mehrnezhad, M., Toreini, E., Shahandashti, S. F., Hao, F. (2015). TouchSignatures: Identification of User Touch Actions Based on Mobile Sensors via JavaScript. PDF

Mehrnezhad, M., Toreini, E., Shahandashti, S. F., Hao, F. (2016). Stealing PINs via Mobile Sensors: Actual Risk versus User Perception. International Journal of Information Security. PDF

Carlini, N., Mishra, P., Vaidya, T., Zhang, Y., Sherr, M., Shields, C., Zhou, W. (2016). Hidden Voice Commands. PDF, slides and Video

Orekondy, T., Schiele, B., Fritz, M. (2017). Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images. arXiv

Upal Mahbub, Sayantan Sarkar, Vishal M. Patel, Rama Chellappa. (2016). Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results. arXiv

Pouya Samangouei, Rama Chellappa. (2016). Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devices. arXiv

CNNs Compression for Mobile Devices

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv

Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <1MB Model Size. arXiv

Karen Ullrich, Edward Meeds, Max Welling. (2017). Soft Weight-Sharing for Neural Network Compression. arXiv

YunheWang, Chang Xu, Shan You, Dacheng Tao, Chao Xu (2016). Compressing Convolutional Neural Networks in the Frequency Domain. PDF

Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen. (2015). Compressing Convolutional Neural Networks. arXiv

Han, S., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv

Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J. (2016). Quantized Convolutional Neural Networks for Mobile Devices. arXiv

Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., Dally, W. J. (2016). EIE: Efficient Inference Engine on Compressed Deep Neural Network. arXiv

Wenjia Meng, Zonghua Gu, Ming Zhang, Zhaohui Wu. (2017). Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices. arXiv

Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini. (2017). YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration. arXiv

Distributed Learning

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., Arcas, B. A. y. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv

Best Practices

(Platform agnostic)

Wujek, B., Hall, P., & Güneș, F. (2016). Best Practices for Machine Learning Applications. PDF

Domingos, P. (2012). A few useful things to know about machine learning. PDF

Leslie N. Smith. Best Practices for Applying Deep Learning to Novel Applications. arXiv

Martin Zinkevich. Rules of Machine Learning: Best Practices for ML Engineering. PDF