| La-MAML: Look-ahead Meta Learning for Continual Learning G Gupta, K Yadav, L Paull Neurips 2020 (Oral), 2020 | 138* | 2020 |
| Geometric consistency for self-supervised end-to-end visual odometry G Iyer, J Krishna Murthy, G Gupta, M Krishna, L Paull Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 62 | 2018 |
| Towards view-invariant intersection recognition from videos using deep network ensembles A Kumar, G Gupta, A Sharma, KM Krishna 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 16 | 2018 |
| ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages A Jesson, C Lu, G Gupta, A Filos, JN Foerster, Y Gal ICML 2024, 2023 | 5 | 2023 |
| Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? G Gupta, TGJ Rudner, RT McAllister, A Gaidon, Y Gal CleaR conference 2023, NeurIPS ML Safety Workshop, 2023 | 5 | 2023 |
| Probabilistic object detection: Strengths, weaknesses, opportunities D Bhatt, D Bansal, G Gupta, H Lee, KM Jatavallabhula, L Paull Workshop on AI for Autonomous Driving at the International Conference on …, 2020 | 4 | 2020 |
| Unifying variational inference and PAC-Bayes for supervised learning that scales S Thakur, H Van Hoof, G Gupta, D Meger arXiv preprint arXiv:1910.10367, 2019 | 4 | 2019 |
| Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control G Gupta, K Yadav, Y Gal, D Batra, Z Kira, C Lu, TGJ Rudner NeurIPS conference 2024 spotlight paper, 2024 | 3 | 2024 |
| Towards View-Invariant Intersection Recognition from Videos using Deep Network A Kumar, G Gupta, A Sharma, KM Krishna | 1 | 2018 |
Karmesh YadavPh.D. Student at Georgia TechEmail confirmado em gatech.edu
