| Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty U Bhatt, J Antorán, Y Zhang, QV Liao, P Sattigeri, R Fogliato, G Melançon, ... Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 401-413, 2021 | 237 | 2021 |
| Improving model calibration with accuracy versus uncertainty optimization R Krishnan, O Tickoo Advances in Neural Information Processing Systems 33, 18237--18248, 2020 | 158 | 2020 |
| Uncertainty-aware audiovisual activity recognition using deep bayesian variational inference M Subedar, R Krishnan, PL Meyer, O Tickoo, J Huang Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 71 | 2019 |
| Specifying weight priors in bayesian deep neural networks with empirical bayes R Krishnan, M Subedar, O Tickoo Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4477-4484, 2020 | 52 | 2020 |
| Devices and methods for accurately identifying objects in a vehicle's environment N Ahuja, I Ndiour, JF Leon, DG Gutierrez, R Krishnan, M Subedar, ... US Patent 11,586,854, 2023 | 37 | 2023 |
| A pocket-sized metabolic analyzer for assessment of resting energy expenditure D Zhao, X Xian, M Terrera, R Krishnan, D Miller, D Bridgeman, K Tao, ... Clinical nutrition 33 (2), 341-347, 2014 | 32 | 2014 |
| Hybrid foreground-background technique for 3d model reconstruction of dynamic scenes R Krishnan, DS Vembar, R Adams, BA Jackson US Patent App. 15/771,750, 2018 | 30 | 2018 |
| BAR: Bayesian activity recognition using variational inference R Krishnan, M Subedar, O Tickoo arXiv preprint arXiv:1811.03305, 2018 | 26 | 2018 |
| Artificial intelligence analysis and explanation utilizing hardware measures of attention K Doshi, M Fisher, R Poornachandran, R Krishnan, C Marshall, N Jain US Patent App. 16/256,844, 2019 | 25 | 2019 |
| Bayesian-torch: Bayesian neural network layers for uncertainty estimation R Krishnan, P Esposito, M Subedar IntelLabs/bayesian-torch, 2022 | 24 | 2022 |
| Devices and methods for updating maps in autonomous driving systems in bandwidth constrained networks R Dorrance, I Alvarez, D Dasalukunte, SMI Alam, S Sharma, K Sivanesan, ... US Patent 11,375,352, 2022 | 17 | 2022 |
| Deep probabilistic models to detect data poisoning attacks M Subedar, N Ahuja, R Krishnan, IJ Ndiour, O Tickoo arXiv preprint arXiv:1912.01206, 2019 | 17 | 2019 |
| A multi-step nonlinear dimension-reduction approach with applications to big data R Krishnan, VA Samaranayake, S Jagannathan IEEE Transactions on Knowledge and Data Engineering 31 (12), 2249-2261, 2018 | 17 | 2018 |
| Methods and apparatus to obtain well-calibrated uncertainty in Deep Neural Networks R Krishnan, O Tickoo, N Ahuja, I Ndiour, M Subedar US Patent App. 17/133,072, 2021 | 14 | 2021 |
| Efficient priors for scalable variational inference in Bayesian deep neural networks R Krishnan, M Subedar, O Tickoo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 13 | 2019 |
| A hierarchical dimension reduction approach for big data with application to fault diagnostics R Krishnan, VA Samaranayake, S Jagannathan Big Data Research 18, 100121, 2019 | 12 | 2019 |
| Mitigating Sampling Bias and Improving Robustness in Active Learning R Krishnan, A Sinha, N Ahuja, M Subedar, O Tickoo, R Iyer ICML 2021 Workshop on Human in the Loop Learning, 2021 | 8 | 2021 |
| 3D scene reconstruction using shared semantic knowledge IJ Alvarez, R Krishnan US Patent 10,217,292, 2019 | 8 | 2019 |
| Meta continual learning via dynamic programming R Krishnan, P Balaprakash arXiv preprint arXiv:2008.02219, 2020 | 7 | 2020 |
| Continual active adaptation to evolving distributional shifts A Machireddy, R Krishnan, N Ahuja, O Tickoo Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 6 | 2022 |
Omesh TickooIntel Corporation在 intel.com 的电子邮件经过验证
