| Flower: A Friendly Federated Learning Research Framework DJ Beutel, T Topal, A Mathur, X Qiu, T Parcollet, P P. B. de Gusmão, ... arXiv preprint arXiv:2007.14390, 2020 | 867 | 2020 |
| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 347 | 2024 |
| 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 | 241 | 2017 |
| 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 Proceedings of the 15th Annual International Conference on Mobile Systems …, 2017 | 171 | 2017 |
| Improving literacy in rural India: Cellphone games in an after-school program M Kam, A Kumar, S Jain, A Mathur, J Canny 2009 International Conference on Information and Communication Technologies …, 2009 | 152 | 2009 |
| Earables for personal-scale behavior analytics F Kawsar, C Min, A Mathur, A Montanari IEEE Pervasive Computing 17 (3), 83-89, 2018 | 151 | 2018 |
| Designing digital games for rural children: a study of traditional village games in India M Kam, A Mathur, A Kumar, J Canny Proceedings of the 27th international conference on Human factors in …, 2009 | 136 | 2009 |
| Designing e-learning games for rural children in India: a format for balancing learning with fun M Kam, A Agarwal, A Kumar, S Lal, A Mathur, A Tewari, J Canny Proceedings of the 7th ACM conference on Designing interactive systems, 58-67, 2008 | 123 | 2008 |
| A systematic study of unsupervised domain adaptation for robust human-activity recognition Y Chang, A Mathur, A Isopoussu, J Song, F Kawsar Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020 | 116 | 2020 |
| Collossl: Collaborative self-supervised learning for human activity recognition Y Jain, CI Tang, C Min, F Kawsar, A Mathur Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2022 | 86 | 2022 |
| A first look into the carbon footprint of federated learning X Qiu, T Parcollet, J Fernandez-Marques, PPB Gusmao, Y Gao, DJ Beutel, ... Journal of Machine Learning Research 24 (129), 1-23, 2023 | 80 | 2023 |
| Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices A Mathur, T Zhang, S Bhattacharya, P Velickovic, L Joffe, ND Lane, ... 2018 17th ACM/IEEE international conference on information processing in …, 2018 | 79 | 2018 |
| Tiny habits in the giant enterprise: understanding the dynamics of a quantified workplace A Mathur, M Van den Broeck, G Vanderhulst, A Mashhadi, F Kawsar Proceedings of the 2015 ACM International Joint Conference on Pervasive and …, 2015 | 78 | 2015 |
| Mic2mic: using cycle-consistent generative adversarial networks to overcome microphone variability in speech systems A Mathur, A Isopoussu, F Kawsar, N Berthouze, ND Lane Proceedings of the 18th international conference on information processing …, 2019 | 63 | 2019 |
| Engagement-aware computing: Modelling user engagement from mobile contexts A Mathur, ND Lane, F Kawsar Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 63 | 2016 |
| The myth of subtle notifications A Mashhadi, A Mathur, F Kawsar Proceedings of the 2014 ACM International Joint Conference on Pervasive and …, 2014 | 63 | 2014 |
| Resource characterisation of personal-scale sensing models on edge accelerators M Antonini, TH Vu, C Min, A Montanari, A Mathur, F Kawsar Proceedings of the First International Workshop on Challenges in Artificial …, 2019 | 62 | 2019 |
| Exploring audio and kinetic sensing on earable devices C Min, A Mathur, F Kawsar Proceedings of the 4th ACM Workshop on Wearable Systems and Applications, 5-10, 2018 | 54 | 2018 |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering ES Lubana, CI Tang, F Kawsar, RP Dick, A Mathur ICML (International Conference on Machine Learning), 2022 | 52 | 2022 |
| 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 | 47 | 2016 |
Fahim KawsarBell Labs and University of GlasgowVerified email at nokia-bell-labs.com
