| GDumb: A Simple Approach that Questions Our Progress in Continual Learning A Prabhu, PHS Torr, PK Dokania Proceedings of the European Conference on Computer Vision (ECCV) 2020, 2020 | 567 | 2020 |
| Towards sub-word level compositions for sentiment analysis of hindi-english code mixed text A Joshi, A Prabhu, M Shrivastava, V Varma Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 204* | 2016 |
| Inverse scaling: When bigger isn't better IR McKenzie, A Lyzhov, M Pieler, A Parrish, A Mueller, A Prabhu, ... arXiv preprint arXiv:2306.09479, 2023 | 96* | 2023 |
| Simple unsupervised multi-object tracking S Karthik, A Prabhu, V Gandhi arXiv preprint arXiv:2006.02609, 2020 | 94 | 2020 |
| Deep expander networks: Efficient deep networks from graph theory A Prabhu, G Varma, A Namboodiri Proceedings of the European Conference on Computer Vision (ECCV), 20-35, 2018 | 86 | 2018 |
| Sampling Bias in Deep Active Classification: An Empirical Study A Prabhu, C Dognin, M Singh 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP …, 2019 | 65 | 2019 |
| Towards deep learning in hindi ner: An approach to tackle the labelled data scarcity V Athavale, S Bharadwaj, M Pamecha, A Prabhu, M Shrivastava arXiv preprint arXiv:1610.09756, 2016 | 62 | 2016 |
| Computationally budgeted continual learning: What does matter? A Prabhu, HA Al Kader Hammoud, PK Dokania, PHS Torr, SN Lim, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 49 | 2023 |
| Real-time evaluation in online continual learning: A new hope Y Ghunaim, A Bibi, K Alhamoud, M Alfarra, HA Al Kader Hammoud, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 46* | 2023 |
| Towards adversarial evaluations for inexact machine unlearning S Goel, A Prabhu, A Sanyal, SN Lim, P Torr, P Kumaraguru arXiv preprint arXiv:2201.06640, 2022 | 29* | 2022 |
| Online continual learning without the storage constraint A Prabhu, Z Cai, P Dokania, P Torr, V Koltun, O Sener arXiv preprint arXiv:2305.09253, 2023 | 22 | 2023 |
| Hybrid binary networks: optimizing for accuracy, efficiency and memory A Prabhu, V Batchu, R Gajawada, SA Munagala, A Namboodiri 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 821-829, 2018 | 17 | 2018 |
| No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks S Karthik, A Prabhu, PK Dokania, V Gandhi International Conference on Learning Representations (ICLR), 2021, 2021 | 15 | 2021 |
| No" zero-shot" without exponential data: Pretraining concept frequency determines multimodal model performance V Udandarao, A Prabhu, A Ghosh, Y Sharma, PHS Torr, A Bibi, S Albanie, ... arXiv preprint arXiv:2404.04125, 2024 | 12 | 2024 |
| Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? HA Al Kader Hammoud, A Prabhu, SN Lim, PHS Torr, A Bibi, B Ghanem Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 11* | 2023 |
| Corrective machine unlearning S Goel, A Prabhu, P Torr, P Kumaraguru, A Sanyal arXiv preprint arXiv:2402.14015, 2024 | 8 | 2024 |
| Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress A Prabhu, V Udandarao, P Torr, M Bethge, A Bibi, S Albanie arXiv preprint arXiv:2402.19472, 2024 | 3 | 2024 |
| STQ-Nets: Unifying Network Binarization and Structured Pruning SA Munagala, A Prabhu, A Namboodiri Proceedings of the British Machine Vision Conference (BMVC) 2020, 2020 | 3 | 2020 |
| Distribution-aware binarization of neural networks for sketch recognition A Prabhu, V Batchu, SA Munagala, R Gajawada, A Namboodiri 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 830-838, 2018 | 3 | 2018 |
| Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry S Sinha, A Prabhu, P Kumaraguru, S Bhat, M Bethge arXiv preprint arXiv:2404.06405, 2024 | 2 | 2024 |
Philip TorrProfessor, University of Oxford在 eng.ox.ac.uk 的电子邮件经过验证
