| The lottery ticket hypothesis: Finding sparse, trainable neural networks J Frankle, M Carbin arXiv preprint arXiv:1803.03635, 2018 | 4339 | 2018 |
| Linear mode connectivity and the lottery ticket hypothesis J Frankle, GK Dziugaite, D Roy, M Carbin International Conference on Machine Learning, 3259-3269, 2020 | 894* | 2020 |
| Automatically patching errors in deployed software JH Perkins, S Kim, S Larsen, S Amarasinghe, J Bachrach, M Carbin, ... Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles …, 2009 | 521 | 2009 |
| Dynamic knobs for responsive power-aware computing H Hoffmann, S Sidiroglou, M Carbin, S Misailovic, A Agarwal, M Rinard ACM SIGARCH computer architecture news 39 (1), 199-212, 2011 | 450 | 2011 |
| Comparing rewinding and fine-tuning in neural network pruning A Renda, J Frankle, M Carbin International Conference on Learning Representations, 2020 | 449 | 2020 |
| The lottery ticket hypothesis for pre-trained bert networks T Chen, J Frankle, S Chang, S Liu, Y Zhang, Z Wang, M Carbin Advances in neural information processing systems 33, 15834-15846, 2020 | 408 | 2020 |
| Verifying quantitative reliability for programs that execute on unreliable hardware M Carbin, S Misailovic, MC Rinard ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages …, 2013 | 346 | 2013 |
| Using Datalog with binary decision diagrams for program analysis J Whaley, D Avots, M Carbin, MS Lam Asian Symposium on Programming Languages and Systems, 97-118, 2005 | 294 | 2005 |
| Pruning neural networks at initialization: Why are we missing the mark? J Frankle, GK Dziugaite, DM Roy, M Carbin International Conference on Learning Representations, 2020 | 256 | 2020 |
| Context-sensitive program analysis as database queries MS Lam, J Whaley, VB Livshits, MC Martin, D Avots, M Carbin, C Unkel Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on …, 2005 | 248 | 2005 |
| Chisel: Reliability-and accuracy-aware optimization of approximate computational kernels S Misailovic, M Carbin, S Achour, Z Qi, MC Rinard OOPSLA 49 (10), 309-328, 2014 | 243 | 2014 |
| Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks C Mendis, A Renda, S Amarasinghe, M Carbin International Conference on machine learning, 4505-4515, 2019 | 196 | 2019 |
| The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models T Chen, J Frankle, S Chang, S Liu, Y Zhang, M Carbin, Z Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 142 | 2021 |
| Proving acceptability properties of relaxed nondeterministic approximate programs M Carbin, D Kim, S Misailovic, MC Rinard PLDI: Programming Languages Design and Implementation 47 (6), 169-180, 2012 | 139 | 2012 |
| Detecting and escaping infinite loops with jolt M Carbin, S Misailovic, M Kling, MC Rinard ECOOP 2011–Object-Oriented Programming: 25th European Conference, Lancaster …, 2011 | 108 | 2011 |
| Exploiting errors for efficiency: A survey from circuits to applications P Stanley-Marbell, A Alaghi, M Carbin, E Darulova, L Dolecek, ... ACM Computing Surveys (CSUR) 53 (3), 1-39, 2020 | 89 | 2020 |
| Automatically identifying critical input regions and code in applications M Carbin, MC Rinard Proceedings of the 19th international symposium on Software testing and …, 2010 | 80 | 2010 |
| Bypass virtualization IITJ Purtell, W Chun, M Carbin US Patent 8,065,687, 2011 | 75 | 2011 |
| Automatic input rectification F Long, V Ganesh, M Carbin, S Sidiroglou, M Rinard 2012 34th International Conference on Software Engineering (ICSE), 80-90, 2012 | 74 | 2012 |
| The three pillars of machine programming J Gottschlich, A Solar-Lezama, N Tatbul, M Carbin, M Rinard, R Barzilay, ... Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine …, 2018 | 72 | 2018 |
Jonathan FrankleDatabricksVerified email at databricks.com
