| A many-core architecture for in-memory data processing SR Agrawal, S Idicula, A Raghavan, E Vlachos, V Govindaraju, ... Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 61 | 2017 |
| Rhythm: Harnessing data parallel hardware for server workloads SR Agrawal, V Pistol, J Pang, J Tran, D Tarjan, AR Lebeck ACM SIGPLAN Notices 49 (4), 19-34, 2014 | 53 | 2014 |
| Oracle automl: a fast and predictive automl pipeline A Yakovlev, HF Moghadam, A Moharrer, J Cai, N Chavoshi, ... Proceedings of the VLDB Endowment 13 (12), 3166-3180, 2020 | 38 | 2020 |
| Algorithm-specific neural network architectures for automatic machine learning model selection S Agrawal, S Idicula, V Varadarajan, N Agarwal US Patent 11,544,494, 2023 | 30 | 2023 |
| Gradient-based auto-tuning for machine learning and deep learning models V Varadarajan, S Idicula, S Agrawal, N Agarwal US Patent 11,176,487, 2021 | 23 | 2021 |
| Rapid: In-memory analytical query processing engine with extreme performance per watt C Balkesen, N Kunal, G Giannikis, P Fender, S Sundara, F Schmidt, ... Proceedings of the 2018 International Conference on Management of Data, 1407 …, 2018 | 23 | 2018 |
| Using meta-learning for automatic gradient-based hyperparameter optimization for machine learning and deep learning models V Varadarajan, S Agrawal, S Idicula, N Agarwal US Patent App. 15/914,883, 2019 | 19 | 2019 |
| Scalable and efficient distributed auto-tuning of machine learning and deep learning models V Varadarajan, S Idicula, S Agrawal, N Agarwal US Patent 11,120,368, 2021 | 17 | 2021 |
| Exploiting accelerators for efficient high dimensional similarity search SR Agrawal, CM Dee, AR Lebeck ACM SIGPLAN Notices 51 (8), 1-12, 2016 | 17 | 2016 |
| Using hyperparameter predictors to improve accuracy of automatic machine learning model selection HF Moghadam, S Agrawal, V Varadarajan, A Yakovlev, S Idicula, ... US Patent 11,620,568, 2023 | 12 | 2023 |
| Predicting machine learning or deep learning model training time A Yakovlev, V Varadarajan, S Agrawal, HF Moghadam, S Idicula, ... US Patent 11,429,895, 2022 | 11 | 2022 |
| Memory management for sparse matrix multiplication SR Agrawal, S Idicula, N Agarwal US Patent US10452744B2, 2019 | 11 | 2019 |
| Artist Identification for Renaissance Paintings J Jou, S Agrawal | 11 | 2011 |
| Fast, predictive, and iteration-free automated machine learning pipeline V Varadarajan, SR Agrawal, HF Moghadam, A Yakovlev, A Moharrer, ... US Patent App. 17/086,204, 2021 | 9 | 2021 |
| Matrix multiplication at memory bandwidth A Raghavan, SR Agrawal, S Idicula, N Agarwal US Patent 10,521,225, 2019 | 6 | 2019 |
| Automated machine learning pipeline for timeseries datasets utilizing point-based algorithms N Chavoshi, A Yakovlev, HF Moghadam, V Varadarajan, S Agrawal, ... US Patent App. 17/071,285, 2022 | 5 | 2022 |
| Big data processing: Scalability with extreme single-node performance V Govindaraju, S Idicula, S Agrawal, V Vardarajan, A Raghavan, J Wen, ... 2017 IEEE International Congress on Big Data (BigData Congress), 129-136, 2017 | 4 | 2017 |
| Separation maximization technique for anomaly scores to compare anomaly detection models HF Moghadam, A Yakovlev, S Agrawal, V Varadarajan, R Hopkins, ... US Patent App. 17/083,536, 2022 | 2 | 2022 |
| Memory usage prediction for machine learning and deep learning models A Moharrer, SR Agrawal, V Varadarajan, S Jinturkar, N Agarwal US Patent App. 16/985,116, 2022 | 2 | 2022 |
| Assymetric allocation of SRAM and data layout for efficient matrix multiplication G Chadha, S Idicula, S Agrawal, N Agarwal US Patent 11,138,291, 2021 | 2 | 2021 |
Sam IdiculaOracleVerified email at oracle.com
