| Biological underpinnings for lifelong learning machines D Kudithipudi, M Aguilar-Simon, J Babb, M Bazhenov, D Blackiston, ... Nature Machine Intelligence 4 (3), 196-210, 2022 | 232 | 2022 |
| Time-series learning of latent-space dynamics for reduced-order model closure R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu Physica D: Nonlinear Phenomena 405, 132368, 2020 | 138 | 2020 |
| A Bayesian approach to selecting hyperelastic constitutive models of soft tissue S Madireddy, B Sista, K Vemaganti Computer Methods in Applied Mechanics and Engineering 291, 102-122, 2015 | 94 | 2015 |
| Applications and techniques for fast machine learning in science AMC Deiana, N Tran, J Agar, M Blott, G Di Guglielmo, J Duarte, P Harris, ... Frontiers in big Data 5, 787421, 2022 | 66 | 2022 |
| DeepMerge–II. Building robust deep learning algorithms for merging galaxy identification across domains A Ćiprijanović, D Kafkes, K Downey, S Jenkins, GN Perdue, S Madireddy, ... Monthly Notices of the Royal Astronomical Society 506 (1), 677-691, 2021 | 59 | 2021 |
| Bayesian calibration of hyperelastic constitutive models of soft tissue S Madireddy, B Sista, K Vemaganti Journal of the Mechanical Behavior of Biomedical Materials, Accepted for …, 2016 | 53 | 2016 |
| Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction LL Lao, S Kruger, C Akcay, P Balaprakash, TA Bechtel, E Howell, J Koo, ... Plasma Physics and Controlled Fusion 64 (7), 074001, 2022 | 45 | 2022 |
| DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection A Ćiprijanović, A Lewis, K Pedro, S Madireddy, B Nord, GN Perdue, ... Machine Learning: Science and Technology 4 (2), 025013, 2023 | 41* | 2023 |
| Scaling transformer neural networks for skillful and reliable medium-range weather forecasting T Nguyen, R Shah, H Bansal, T Arcomano, R Maulik, V Kotamarthi, ... arXiv preprint arXiv:2312.03876, 2023 | 40* | 2023 |
| HPC I/O throughput bottleneck analysis with explainable local models M Isakov, E Del Rosario, S Madireddy, P Balaprakash, P Carns, RB Ross, ... SC20: International Conference for High Performance Computing, Networking …, 2020 | 37 | 2020 |
| Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time S Lee, Q Kang, S Madireddy, P Balaprakash, A Agrawal, A Choudhary, ... 2019 IEEE International Conference on Big Data (Big Data), 830-839, 2019 | 34 | 2019 |
| Machine learning based parallel I/O predictive modeling: A case study on Lustre file systems S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ... High Performance Computing: 33rd International Conference, ISC High …, 2018 | 31 | 2018 |
| A domain-agnostic approach for characterization of lifelong learning systems MM Baker, A New, M Aguilar-Simon, Z Al-Halah, SMR Arnold, ... Neural Networks 160, 274-296, 2023 | 25 | 2023 |
| Analysis and correlation of application I/O performance and system-wide I/O activity S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ... 2017 International Conference on Networking, Architecture, and Storage (NAS …, 2017 | 25 | 2017 |
| DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. A Ciprijanovic, D Kafkes, GF Snyder, FJ Sánchez, GN Perdue, K Pedro, ... Mach. Learn. Sci. Technol. 3 (3), 35007, 2022 | 22 | 2022 |
| Phase segmentation in atom-probe tomography using deep learning-based edge detection S Madireddy, DW Chung, T Loeffler, SKRS Sankaranarayanan, ... Scientific reports 9 (1), 20140, 2019 | 21 | 2019 |
| Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures S Madireddy, A Yanguas-Gil, P Balaprakash arXiv preprint arXiv:2308.04539, 2023 | 16* | 2023 |
| Adaptive Learning for Concept Drift in Application Performance Modeling S Madireddy, P Balaprakash, P Carns, R Latham, GK Lockwood, R Ross, ... Proceedings of the 48th International Conference on Parallel Processing, 1-11, 2019 | 16 | 2019 |
| Modeling I/O performance variability using conditional variational autoencoders S Madireddy, P Balaprakash, P Carns, R Latham, R Ross, S Snyder, ... 2018 IEEE international conference on cluster computing (CLUSTER), 109-113, 2018 | 14 | 2018 |
| Hpc storage service autotuning using variational-autoencoder-guided asynchronous bayesian optimization M Dorier, R Egele, P Balaprakash, J Koo, S Madireddy, S Ramesh, ... 2022 IEEE International Conference on Cluster Computing (CLUSTER), 381-393, 2022 | 13 | 2022 |
Prasanna BalaprakashDirector of AI Programs and Distinguished R&D Staff Scientist, Oak Ridge National LaboratoryZweryfikowany adres z ornl.gov
