| DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices ND Lane, S Bhattacharya, P Georgiev, C Forlivesi, L Jiao, L Qendro, ... International Conference on Information Processing in Sensor Networks (IPSN '16), 2016 | 290 | 2016 |
| Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition A Stisen, H Blunck, S Bhattacharya, TS Prentow, MB Kjærgaard, A Dey, ... The 13th ACM Conference on Embedded Networked Sensor Systems, 2015 | 277 | 2015 |
| An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices ND Lane, S Bhattacharya, P Georgiev, C Forlivesi, F Kawsar Proceedings of the 2015 International Workshop on Internet of Things towards …, 2015 | 150 | 2015 |
| Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables S Bhattacharya, ND Lane ACM Conference on Embedded Networked Sensor Systems (SenSys) 2016, 2016 | 124 | 2016 |
| Energy-efficient trajectory tracking for mobile devices MB Kjærgaard, S Bhattacharya, H Blunck, P Nurmi Proceedings of the 9th international conference on Mobile systems …, 2011 | 110 | 2011 |
| From Smart to Deep: Robust Activity Recognition on Smartwatches using Deep Learning S Bhattacharya, ND Lane | 105 | 2016 |
| A survey on different video watermarking techniques and comparative analysis with reference to H. 264/AVC S Bhattacharya, T Chattopadhyay, A Pal 2006 IEEE International Symposium on Consumer Electronics, 1-6, 2006 | 99 | 2006 |
| Using unlabeled data in a sparse-coding framework for human activity recognition S Bhattacharya, P Nurmi, N Hammerla, T Plötz Pervasive and Mobile Computing 15, 242-262, 2014 | 95 | 2014 |
| Squeezing deep learning into mobile and embedded devices ND Lane, S Bhattacharya, A Mathur, P Georgiev, C Forlivesi, F Kawsar IEEE Pervasive Computing 16 (3), 82-88, 2017 | 92 | 2017 |
| Towards multimodal deep learning for activity recognition on mobile devices V Radu, ND Lane, S Bhattacharya, C Mascolo, MK Marina, F Kawsar Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 83 | 2016 |
| Identifying meaningful places: The non-parametric way P Nurmi, S Bhattacharya International Conference on Pervasive Computing, 111-127, 2008 | 81 | 2008 |
| Multimodal Deep Learning for Activity and Context Recognition V Radu, C Tong, S Bhattacharya, ND Lane, C Mascolo, MK Marina, ... Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2018 | 72 | 2018 |
| The company you keep: mobile malware infection rates and inexpensive risk indicators HTT Truong, E Lagerspetz, P Nurmi, AJ Oliner, S Tarkoma, N Asokan, ... Proceedings of the 23rd international conference on World wide web, 39-50, 2014 | 71 | 2014 |
| DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware A Mathur, ND Lane, S Bhattacharya, A Boran, C Forlivesi, F Kawsar The 15th International Conference on Mobile Systems, Applications and …, 2017 | 63 | 2017 |
| Robust and Energy-Efficient Trajectory Tracking for Mobile Devices S Bhattacharya, H Blunck, MB Kjærgaard, P Nurmi IEEE Transactions on Mobile Computing, 2014 | 44 | 2014 |
| A grid-based algorithm for on-device GSM positioning P Nurmi, S Bhattacharya, J Kukkonen Proceedings of the 12th ACM international conference on Ubiquitous computing …, 2010 | 43 | 2010 |
| Influence of landmark-based navigation instructions on user attention in indoor smart spaces P Nurmi, A Salovaara, S Bhattacharya, T Pulkkinen, G Kahl Proceedings of the 16th international conference on Intelligent user …, 2011 | 25 | 2011 |
| Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations P Georgiev, S Bhattacharya, N Lane, C Mascolo Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017 | 21 | 2017 |
| Gaussian process-based predictive modeling for bus ridership S Bhattacharya, S Phithakkitnukoon, P Nurmi, A Klami, M Veloso, C Bento Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing …, 2013 | 21 | 2013 |
| DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit. ND Lane, S Bhattacharya, A Mathur, C Forlivesi, F Kawsar MobiCASE, 98-107, 2016 | 20 | 2016 |
Nicholas LaneUniversity of Cambridge and Samsung AIVerified email at cam.ac.uk
