| On mixup training: Improved calibration and predictive uncertainty for deep neural networks S Thulasidasan, G Chennupati, JA Bilmes, T Bhattacharya, S Michalak Advances in neural information processing systems 32, 2019 | 617 | 2019 |
| Combating label noise in deep learning using abstention S Thulasidasan, T Bhattacharya, J Bilmes, G Chennupati, J Mohd-Yusof arXiv preprint arXiv:1905.10964, 2019 | 216 | 2019 |
| Quantum algorithm implementations for beginners A Adedoyin, J Ambrosiano, P Anisimov, W Casper, G Chennupati, ... arXiv preprint arXiv:1804.03719, 2018 | 194 | 2018 |
| Quantum algorithm implementations for beginners PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ... arXiv, arXiv: 1804.03719, 2018 | 147 | 2018 |
| Verified instruction-level energy consumption measurement for nvidia gpus Y Arafa, A ElWazir, A ElKanishy, Y Aly, A Elsayed, AH Badawy, ... Proceedings of the 17th ACM International Conference on Computing Frontiers …, 2020 | 54 | 2020 |
| Ppt-gpu: Scalable gpu performance modeling Y Arafa, AHA Badawy, G Chennupati, N Santhi, S Eidenbenz IEEE Computer Architecture Letters 18 (1), 55-58, 2019 | 46 | 2019 |
| An effective baseline for robustness to distributional shift S Thulasidasan, S Thapa, S Dhaubhadel, G Chennupati, T Bhattacharya, ... 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 45 | 2021 |
| Finding the number of latent topics with semantic non-negative matrix factorization R Vangara, M Bhattarai, E Skau, G Chennupati, H Djidjev, T Tierney, ... IEEE access 9, 117217-117231, 2021 | 37 | 2021 |
| Low overhead instruction latency characterization for nvidia gpgpus Y Arafa, AHA Badawy, G Chennupati, N Santhi, S Eidenbenz 2019 IEEE High Performance Extreme Computing Conference (HPEC), 1-8, 2019 | 37 | 2019 |
| Code characterization with graph convolutions and capsule networks P Haridas, G Chennupati, N Santhi, P Romero, S Eidenbenz IEEE Access 8, 136307-136315, 2020 | 28 | 2020 |
| Hybrid, scalable, trace-driven performance modeling of GPGPUs Y Arafa, AH Badawy, A ElWazir, A Barai, A Eker, G Chennupati, N Santhi, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 27 | 2021 |
| Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles Y Arafa, AH Badawy, G Chennupati, A Barai, N Santhi, S Eidenbenz Proceedings of the 34th ACM International Conference on Supercomputing, 1-12, 2020 | 27 | 2020 |
| Distributed non-negative matrix factorization with determination of the number of latent features G Chennupati, R Vangara, E Skau, H Djidjev, B Alexandrov The Journal of Supercomputing 76, 7458-7488, 2020 | 27 | 2020 |
| Parallel application performance prediction using analysis based models and HPC simulations MA Obaida, J Liu, G Chennupati, N Santhi, S Eidenbenz Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced …, 2018 | 25 | 2018 |
| Semantic nonnegative matrix factorization with automatic model determination for topic modeling R Vangara, E Skau, G Chennupati, H Djidjev, T Tierney, JP Smith, ... 2020 19th IEEE International Conference on Machine Learning and Applications …, 2020 | 23 | 2020 |
| A scalable analytical memory model for CPU performance prediction G Chennupati, N Santhi, R Bird, S Thulasidasan, AHA Badawy, S Misra, ... High Performance Computing Systems. Performance Modeling, Benchmarking, and …, 2018 | 21 | 2018 |
| Distributed non-negative tensor train decomposition M Bhattarai, G Chennupati, E Skau, R Vangara, H Djidjev, BS Alexandrov 2020 IEEE High Performance Extreme Computing Conference (HPEC), 1-10, 2020 | 20 | 2020 |
| An analytical memory hierarchy model for performance prediction G Chennupati, N Santhi, S Eidenbenz, S Thulasidasan 2017 Winter Simulation Conference (WSC), 908-919, 2017 | 20 | 2017 |
| Performance optimization of multi-core grammatical evolution generated parallel recursive programs G Chennupati, RMA Azad, C Ryan Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 20 | 2015 |
| Scalable performance prediction of codes with memory hierarchy and pipelines G Chennupati, N Santhi, S Eidenbenz Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced …, 2019 | 18 | 2019 |
Stephan EidenbenzComputer Science, Los Alamos National LaboratoryZweryfikowany adres z lanl.gov
