| Learning deep networks from noisy labels with dropout regularization I Jindal, M Nokleby, X Chen 2016 IEEE 16th International Conference on Data Mining (ICDM), 967-972, 2016 | 241 | 2016 |
| A unified neural network approach for estimating travel time and distance for a taxi trip I Jindal, X Chen, M Nokleby, J Ye arXiv preprint arXiv:1710.04350, 2017 | 105 | 2017 |
| Optimizing taxi carpool policies via reinforcement learning and spatio-temporal mining I Jindal, ZT Qin, X Chen, M Nokleby, J Ye 2018 IEEE International Conference on Big Data (Big Data), 1417-1426, 2018 | 77 | 2018 |
| Nl-augmenter: A framework for task-sensitive natural language augmentation KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ... Northern European Journal of Language Technology, 2021 | 73 | 2021 |
| An Effective Label Noise Model for DNN Text Classification I Jindal, D Pressel, B Lester, M Nokleby NAACL-HLT 2019, 2019 | 52 | 2019 |
| Universal proposition bank 2.0 I Jindal, A Rademaker, M Ulewicz, H Linh, H Nguyen, KN Tran, H Zhu, ... Proceedings of the Thirteenth Language Resources and Evaluation Conference …, 2022 | 22 | 2022 |
| Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ... Joniak, Denis Kleyko, Venelin Kovatchev, and et al, 2021 | 18 | 2021 |
| SA-CNN: Dynamic scene classification using convolutional neural networks A Gangopadhyay, SM Tripathi, I Jindal, S Raman arXiv preprint arXiv:1502.05243, 2015 | 17 | 2015 |
| Deep reinforcement learning for optimizing carpooling policies I Jindal, Z Qin, X Chen, M Nokleby, J Ye US Patent App. 15/970,425, 2019 | 16 | 2019 |
| Dynamic scene classification using convolutional neural networks A Gangopadhyay, SM Tripathi, I Jindal, S Raman 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2016 | 15 | 2016 |
| Echocardiographic image denoising using extreme total variation bilateral filter N Biradar, ML Dewal, MK Rohit, I Jindal Optik 127 (1), 30-38, 2016 | 12 | 2016 |
| Beyond labels: Empowering human annotators with natural language explanations through a novel active-learning architecture B Yao, I Jindal, L Popa, Y Katsis, S Ghosh, L He, Y Lu, S Srivastava, Y Li, ... arXiv preprint arXiv:2305.12710, 2023 | 10 | 2023 |
| Improved semantic role labeling using parameterized neighborhood memory adaptation I Jindal, R Aharonov, S Brahma, H Zhu, Y Li arXiv preprint arXiv:2011.14459, 2020 | 9 | 2020 |
| When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications K Pei, I Jindal, KCC Chang, C Zhai, Y Li ACL 2023, 2023 | 8 | 2023 |
| A Nonlinear, Noise-aware, Quasi-clustering Approach to Learning Deep CNNs from Noisy Labels. I Jindal, MS Nokleby, D Pressel, X Chen CVPR Workshops, 64-72, 2019 | 8 | 2019 |
| Label definitions improve semantic role labeling L Zhang, I Jindal, Y Li Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 7 | 2022 |
| Meaning representations for natural languages: Design, models and applications J Flanigan, I Jindal, Y Li, T O’Gorman, M Palmer, N Xue Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 5 | 2022 |
| Beyond labels: Empowering human with natural language explanations through a novel active-learning architecture B Yao, I Jindal, L Popa, Y Katsis, S Ghosh, L He, Y Lu, S Srivastava, ... Association for Computational Linguistics, 2023 | 3 | 2023 |
| A comparative analysis between human-in-the-loop systems and large language models for pattern extraction tasks M Hanafi, Y Katsis, I Jindal, L Popa Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop …, 2022 | 3 | 2022 |
| Classification and representation via separable subspaces: Performance limits and algorithms I Jindal, M Nokleby IEEE Journal of Selected Topics in Signal Processing 12 (5), 1015-1030, 2018 | 3 | 2018 |
Matthew NoklebyML for Perception | CruiseVerified email at getcruise.com
