| Mathprompter: Mathematical reasoning using large language models S Imani, L Du, H Shrivastava arXiv preprint arXiv:2303.05398, 2023 | 292 | 2023 |
| ICU mortality prediction: a classification algorithm for imbalanced datasets S Bhattacharya, V Rajan, H Shrivastava Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 91 | 2017 |
| GLAD: Learning sparse graph recovery H Shrivastava, X Chen, B Chen, G Lan, S Aluru, H Liu, L Song arXiv preprint arXiv:1906.00271, 2019 | 45 | 2019 |
| Classification with imbalance: A similarity-based method for predicting respiratory failure H Shrivastava, V Huddar, S Bhattacharya, V Rajan 2015 IEEE international conference on bioinformatics and biomedicine (BIBM …, 2015 | 39 | 2015 |
| GRNUlar: a deep learning framework for recovering Single-Cell gene regulatory networks H Shrivastava, X Zhang, L Song, S Aluru Journal of Computational Biology 29 (1), 27-44, 2022 | 27 | 2022 |
| Echo state speech recognition H Shrivastava, A Garg, Y Cao, Y Zhang, T Sainath ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 27 | 2021 |
| System and method for predicting health condition of a patient H Shrivastava, V Huddar, S Bhattacharya, V Rajan US Patent 11,087,879, 2021 | 22 | 2021 |
| EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks M Aluru, H Shrivastava, SP Chockalingam, S Shivakumar, S Aluru Bioinformatics 38 (5), 1312-1319, 2022 | 21 | 2022 |
| Methods and systems for predicting mortality of a patient S Bhattacharya, V Rajan, H Shrivastava US Patent 10,463,312, 2019 | 16 | 2019 |
| Grnular: gene regulatory network reconstruction using unrolled algorithm from single cell rna-sequencing data H Shrivastava, X Zhang, S Aluru, L Song bioRxiv, 2020.04. 23.058149, 2020 | 14 | 2020 |
| Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification H Shrivastava, E Bart, B Price, H Dai, B Dai, S Aluru Advances in Neural Information Processing Systems 31, 2018 | 14 | 2018 |
| Methods for recovering conditional independence graphs: a survey H Shrivastava, U Chajewska Journal of Artificial Intelligence Research 80, 593-612, 2024 | 13 | 2024 |
| Neural graphical models H Shrivastava, U Chajewska European Conference on Symbolic and Quantitative Approaches with Uncertainty …, 2023 | 13 | 2023 |
| uGLAD: sparse graph recovery by optimizing deep unrolled networks H Shrivastava, U Chajewska, R Abraham, X Chen arXiv preprint arXiv:2205.11610, 2022 | 13 | 2022 |
| A deep learning approach to recover conditional independence graphs H Shrivastava, U Chajewska, R Abraham, X Chen NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022 | 12 | 2022 |
| On Using Inductive Biases for Designing Deep Learning Architectures. H Shrivastava Georgia Institute of Technology, Atlanta, GA, USA, 2021 | 12 | 2021 |
| Neural graph revealers H Shrivastava, U Chajewska Workshop on Machine Learning for Multimodal Healthcare Data, 7-25, 2023 | 8 | 2023 |
| AntMan: sparse low-rank compression to accelerate RNN inference S Rajbhandari, H Shrivastava, Y He arXiv preprint arXiv:1910.01740, 2019 | 8 | 2019 |
| Exploring entities of interest over multiple data sources using knowledge graphs S Panda, H Shrivastava US Patent App. 17/729,957, 2023 | 3 | 2023 |
| [Industry] MathPrompter: Mathematical Reasoning using Large Language Models S Imani, L Du, H Shrivastava The 61st Annual Meeting Of The Association For Computational Linguistics, 2023 | 2 | 2023 |
Srinivas AluruRegents' Professor and Senior Associate Dean, College of Computing, Georgia TechEmail verificata su cc.gatech.edu