Sandeep R Agrawal
Sandeep R Agrawal
Oracle Labs
Verified email at oracle.com
Title
Cited by
Cited by
Year
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
442014
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
372017
Exploiting accelerators for efficient high dimensional similarity search
SR Agrawal, CM Dee, AR Lebeck
ACM SIGPLAN Notices 51 (8), 1-12, 2016
112016
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
102018
Artist Identification for Renaissance Paintings
J Jou, S Agrawal
82011
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
42019
Memory management for sparse matrix multiplication
SR Agrawal, S Idicula, N Agarwal
US Patent US10452744B2, 2019
32019
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
32017
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
22020
Algorithm-specific neural network architectures for automatic machine learning model selection
S Agrawal, S Idicula, V Varadarajan, N Agarwal
US Patent App. 15/884,163, 2019
22019
Gradient-based auto-tuning for machine learning and deep learning models
V Varadarajan, S Idicula, S Agrawal, N Agarwal
US Patent App. 15/885,515, 2019
12019
Harnessing Data Parallel Hardware for Server Workloads
SR Agrawal
Duke University, 2015
12015
Using Metamodeling for Fast and Accurate Hyperparameter optimization of Machine Learning and Deep Learning Models
A Moharrer, V Varadarajan, S Idicula, S Agrawal, N Agarwal
US Patent App. 16/426,530, 2020
2020
Adaptive sampling for imbalance mitigation and dataset size reduction in machine learning
J Cai, S Agrawal, S Idicula, V Varadarajan, A Yakovlev, N Agarwal
US Patent App. 16/718,164, 2020
2020
Using hyperparameter predictors to improve accuracy of automatic machine learning model selection
HF Moghadam, S Agrawal, V Varadarajan, A Yakovlev, S Idicula, ...
US Patent App. 16/388,830, 2020
2020
Predicting machine learning or deep learning model training time
A Yakovlev, V Varadarajan, S Agrawal, HF Moghadam, S Idicula, ...
US Patent App. 16/384,588, 2020
2020
Mini-machine learning
S Agrawal, V Varadarajan, S Idicula, N Agarwal
US Patent App. 16/166,039, 2020
2020
Matrix multiplication at memory bandwidth
A Raghavan, SR Agrawal, S Idicula, N Agarwal
US Patent 10,521,225, 2019
2019
Scalable and efficient distributed auto-tuning of machine learning and deep learning models
V Varadarajan, S Idicula, S Agrawal, N Agarwal
US Patent App. 16/137,719, 2019
2019
Assymetric allocation of sram and data layout for efficient matrix multiplication
G Chadha, S Idicula, S Agrawal, N Agarwal
US Patent App. 15/716,225, 2019
2019
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