| Evaluating learning algorithms: a classification perspective N Japkowicz, M Shah Cambridge University Press, 2011 | 1531 | 2011 |
| Evaluating intensity normalization on MRIs of human brain with multiple sclerosis M Shah, Y Xiao, N Subbanna, S Francis, DL Arnold, DL Collins, T Arbel Medical image analysis 15 (2), 267-282, 2011 | 232 | 2011 |
| Comparative study of deep learning software frameworks S Bahrampour, N Ramakrishnan, L Schott, M Shah arXiv preprint arXiv:1511.06435, 2015 | 229 | 2015 |
| A survey of on-device machine learning: An algorithms and learning theory perspective S Dhar, J Guo, J Liu, S Tripathi, U Kurup, M Shah ACM Transactions on Internet of Things 2 (3), 1-49, 2021 | 200 | 2021 |
| Is It Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving J Guo, U Kurup, M Shah IEEE Transactions on Intelligent Transportation Systems, 2019 | 188 | 2019 |
| Comparative study of caffe, neon, theano, and torch for deep learning S Bahrampour, N Ramakrishnan, L Schott, M Shah | 142 | 2016 |
| Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields Z Karimaghaloo, M Shah, SJ Francis, DL Arnold, DL Collins, T Arbel IEEE transactions on medical imaging 31 (6), 1181-1194, 2012 | 74 | 2012 |
| Feature selection with conjunctions of decision stumps and learning from microarray data M Shah, M Marchand, J Corbeil IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (1), 174-186, 2011 | 73 | 2011 |
| Concept drift detection and adaptation with hierarchical hypothesis testing S Yu, Z Abraham, H Wang, M Shah, Y Wei, JC Príncipe Journal of the Franklin Institute 356 (5), 3187-3215, 2019 | 63 | 2019 |
| Big data and the internet of things M Shah Big data analysis: New algorithms for a new society, 207-237, 2016 | 63 | 2016 |
| Pruning algorithms to accelerate convolutional neural networks for edge applications: A survey J Liu, S Tripathi, U Kurup, M Shah arXiv preprint arXiv:2005.04275, 2020 | 62 | 2020 |
| Performance evaluation in machine learning N Japkowicz, M Shah Machine Learning in Radiation Oncology: Theory and Applications, 41-56, 2015 | 54 | 2015 |
| TRAF6 and IRF7 control HIV replication in macrophages M Sirois, L Robitaille, R Allary, M Shah, CH Woelk, J Estaquier, J Corbeil PLoS One 6 (11), e28125, 2011 | 52 | 2011 |
| MS lesion segmentation using Markov Random Fields N Subbanna, M Shah, SJ Francis, S Narayanan, D Collins, DL Arnold, ... proceedings of international Conference on medical image computing and …, 2009 | 46 | 2009 |
| An architecture for the deployment of statistical models for the big data era J Heit, J Liu, M Shah 2016 IEEE International Conference on Big Data (Big Data), 1377-1384, 2016 | 39 | 2016 |
| Dynamic task offloading in multi-agent mobile edge computing networks J Heydari, V Ganapathy, M Shah 2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019 | 36 | 2019 |
| Variable metric proximal gradient method with diagonal barzilai-borwein stepsize Y Park, S Dhar, S Boyd, M Shah ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 34 | 2020 |
| Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction S Zhang, S Bahrampour, N Ramakrishnan, L Schott, M Shah 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 30 | 2017 |
| Admm based scalable machine learning on spark S Dhar, C Yi, N Ramakrishnan, M Shah 2015 IEEE International Conference on Big Data (Big Data), 1174-1182, 2015 | 30 | 2015 |
| Exploiting text mining techniques in the analysis of execution traces H Pirzadeh, A Hamou-Lhadj, M Shah 2011 27th IEEE International Conference on Software Maintenance (ICSM), 223-232, 2011 | 29 | 2011 |
