| Multi-time attention networks for irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:2101.10318, 2021 | 217 | 2021 |
| Interpolation-prediction networks for irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:1909.07782, 2019 | 183 | 2019 |
| The belebele benchmark: a parallel reading comprehension dataset in 122 language variants L Bandarkar, D Liang, B Muller, M Artetxe, SN Shukla, D Husa, N Goyal, ... arXiv preprint arXiv:2308.16884, 2023 | 69 | 2023 |
| Black-box adversarial attacks with bayesian optimization SN Shukla, AK Sahu, D Willmott, JZ Kolter arXiv preprint arXiv:1909.13857, 2019 | 44 | 2019 |
| 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 | 38 | 2021 |
| 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 | 34 | 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 | 28 | 2020 |
| Heteroscedastic temporal variational autoencoder for irregularly sampled time series SN Shukla, BM Marlin arXiv preprint arXiv:2107.11350, 2021 | 25 | 2021 |
| 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 | 20 | 2020 |
| Learning to localize objects improves spatial reasoning in visual-llms K Ranasinghe, SN Shukla, O Poursaeed, MS Ryoo, TY Lin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 17 | 2024 |
| Estimation of blood pressure from non-invasive data SN Shukla 2017 39th Annual International Conference of the IEEE Engineering in …, 2017 | 17 | 2017 |
| A survey on principles, models and methods for learning from irregularly sampled time series: From discretization to attention and invariance SN Shukla, BM Marlin arXiv preprint, 2020 | 13 | 2020 |
| Modeling irregularly sampled clinical time series SN Shukla, BM Marlin arXiv preprint arXiv:1812.00531, 2018 | 10 | 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 | 6 | 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 |
| Revisiting kernel temporal segmentation as an adaptive tokenizer for long-form video understanding M Afham, SN Shukla, O Poursaeed, P Zhang, A Shah, S Lim Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 4 | 2023 |
| Bayesian-optimization-based query-efficient black-box adversarial attacks SN Shukla, AK Sahu, D Willmott, JZ Kolter US Patent 11,494,639, 2022 | 4 | 2022 |
| 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 | 4 | 2017 |
| Deep Learning Models for Irregularly Sampled and Incomplete Time Series SN Shukla | 1 | 2021 |
| Adversarial distillation of bayesian neural networks SN Shukla, MP Vadera, B Jalaian, BM Marlin | 1 | 2020 |
Benjamin M. MarlinManning College of Information and Computer Sciences, UMass AmherstZweryfikowany adres z umass.edu