Ranganath Krishnan
Ranganath Krishnan
Research Scientist, Intel Labs
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
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
Improving model calibration with accuracy versus uncertainty optimization
R Krishnan, O Tickoo
Advances in Neural Information Processing Systems 33, 18237--18248, 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
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
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
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
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
BAR: Bayesian activity recognition using variational inference
R Krishnan, M Subedar, O Tickoo
arXiv preprint arXiv:1811.03305, 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
Bayesian-torch: Bayesian neural network layers for uncertainty estimation
R Krishnan, P Esposito, M Subedar
Jan, 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
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
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
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
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
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
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
Meta continual learning via dynamic programming
R Krishnan, P Balaprakash
arXiv preprint arXiv:2008.02219, 2020
3D scene reconstruction using shared semantic knowledge
IJ Alvarez, R Krishnan
US Patent 10,217,292, 2019
Robust multimodal sensor fusion for autonomous driving vehicles
N Ahuja, IJ Alvarez, R Krishnan, IJ Ndiour, M Subedar, O Tickoo
US Patent 11,983,625, 2024
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