| Interpolation-prediction networks for irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:1909.07782, 2019 | 113 | 2019 |
| Multi-time attention networks for irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:2101.10318, 2021 | 64 | 2021 |
| Black-box adversarial attacks with bayesian optimization SN Shukla, AK Sahu, D Willmott, JZ Kolter arXiv preprint arXiv:1909.13857, 2019 | 33 | 2019 |
| Noninvasive cuffless blood pressure measurement by vascular transit time SN Shukla, K Kakwani, A Patra, BK Lahkar, VK Gupta, A Jayakrishna, ... 2015 28th International Conference on VLSI Design, 535-540, 2015 | 28 | 2015 |
| A survey on principles, models and methods for learning from irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:2012.00168, 2020 | 17* | 2020 |
| Simple and efficient hard label black-box adversarial attacks in low query budget regimes SN Shukla, AK Sahu, D Willmott, Z Kolter Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 16 | 2021 |
| Estimation of blood pressure from non-invasive data SN Shukla 2017 39th Annual International Conference of the IEEE Engineering in …, 2017 | 12 | 2017 |
| Integrating physiological time series and clinical notes with deep learning for improved ICU mortality prediction SN Shukla, BM Marlin arXiv preprint arXiv:2003.11059, 2020 | 10 | 2020 |
| Hard label black-box adversarial attacks in low query budget regimes SN Shukla, AK Sahu, D Willmott, JZ Kolter arXiv preprint arXiv:2007.07210 2 (5), 13, 2020 | 6 | 2020 |
| Modeling irregularly sampled clinical time series SN Shukla, BM Marlin arXiv preprint arXiv:1812.00531, 2018 | 6 | 2018 |
| Assessing the adversarial robustness of monte carlo and distillation methods for deep bayesian neural network classification MP Vadera, SN Shukla, B Jalaian, BM Marlin arXiv preprint arXiv:2002.02842, 2020 | 5 | 2020 |
| Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series SN Shukla, BM Marlin arXiv preprint arXiv:2107.11350, 2021 | 4 | 2021 |
| Prediction and imputation in irregularly sampled clinical time series data using hierarchical linear dynamical models A Sengupta, AP Prathosh, SN Shukla, V Rajan, CK Reddy 2017 39th Annual International Conference of the IEEE Engineering in …, 2017 | 3 | 2017 |
| Adversarial distillation of bayesian neural networks SN Shukla, MP Vadera, B Jalaian, BM Marlin | 1 | 2020 |
| Bayesian-optimization-based query-efficient black-box adversarial attacks SN Shukla, AK Sahu, D Willmott, JZ Kolter US Patent 11,494,639, 2022 | | 2022 |
| Deep Learning Models for Irregularly Sampled and Incomplete Time Series SN Shukla | | 2021 |
| Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks AK Sahu, SN Shukla, JZ Kolter arXiv preprint arXiv:2010.04205, 2020 | | 2020 |
| Forecasting a patient vital measurement for healthcare analytics A Sengupta, BS Solanki, PA Prasad, V Rajan, KO Sinclair, S Fullerton, ... US Patent 10,559,385, 2020 | | 2020 |
| System and method of modeling irregularly sampled temporal data using Kalman filters A Sengupta, PA Prasad, SN Shukla, V Rajan, K Sinclair, S Fullerton US Patent 10,437,944, 2019 | | 2019 |
| Analysis of Dropout in Deep Networks SN Shukla | | |
Benjamin M. MarlinManning College of Information and Computer Sciences, UMass AmherstVahvistettu sähköpostiosoite verkkotunnuksessa umass.edu
