| Deep learning in the automotive industry: Applications and tools A Luckow, M Cook, N Ashcraft, E Weill, E Djerekarov, B Vorster 2016 IEEE International Conference on Big Data (Big Data), 3759-3768, 2016 | 212 | 2016 |
| Vehicle re-identification: an efficient baseline using triplet embedding R Kuma, E Weill, F Aghdasi, P Sriram 2019 International Joint Conference on Neural Networks (IJCNN), 1-9, 2019 | 154 | 2019 |
| Artificial intelligence and deep learning applications for automotive manufacturing A Luckow, K Kennedy, M Ziolkowski, E Djerekarov, M Cook, E Duffy, ... 2018 IEEE International Conference on Big Data (Big Data), 3144-3152, 2018 | 69 | 2018 |
| Training neural networks for vehicle re-identification FR Kumar, F Aghdasi, P Sriram, E Weill US Patent 11,455,807, 2022 | 51 | 2022 |
| A strong and efficient baseline for vehicle re-identification using deep triplet embedding R Kumar, E Weill, F Aghdasi, P Sriram Journal of Artificial Intelligence and Soft Computing Research 10 (1), 27-45, 2020 | 25 | 2020 |
| Subjective versus objective: classifying analytical models for productive heterogeneous performance prediction VK Pallipuram, MC Smith, N Sarma, R Anand, E Weill, K Sapra The Journal of Supercomputing 71, 162-201, 2015 | 4 | 2015 |
| Edge-Computing Deep Learning-Based Computer Vision Systems E Weill Clemson University, 2018 | 2 | 2018 |
| Training neural networks for vehicle re-identification FR Kumar, F Aghdasi, P Sriram, E Weill US Patent App. 17/890,849, 2022 | | 2022 |
| Scientific application acceleration utilizing heterogeneous architectures E Weill Clemson University, 2014 | | 2014 |
