Sandeep R Agrawal
Sandeep R Agrawal
Oracle Labs
Bestätigte E-Mail-Adresse bei oracle.com
Titel
Zitiert von
Zitiert von
Jahr
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
422014
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
302017
Exploiting accelerators for efficient high dimensional similarity search
SR Agrawal, CM Dee, AR Lebeck
ACM SIGPLAN Notices 51 (8), 1-12, 2016
102016
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
72018
Artist Identification for Renaissance Paintings
J Jou, S Agrawal
52011
Memory management for sparse matrix multiplication
SR Agrawal, S Idicula, N Agarwal
US Patent US10452744B2, 2019
32019
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
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
12020
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
12019
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, Durham, NC, USA, 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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