| 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 | 864 | 2020 |
| Humanity's last exam L Phan, A Gatti, Z Han, N Li, J Hu, H Zhang, CBC Zhang, M Shaaban, ... arXiv preprint arXiv:2501.14249, 2025 | 301 | 2025 |
| 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 | 244* | 2016 |
| Inverse scaling: When bigger isn't better IR McKenzie, A Lyzhov, M Pieler, A Parrish, A Mueller, A Prabhu, ... Transactions of Machine Learning Research (TMLR), 2023 | 228* | 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 | 116* | 2022 |
| Simple unsupervised multi-object tracking S Karthik, A Prabhu, V Gandhi arXiv preprint arXiv:2006.02609, 2020 | 109 | 2020 |
| Real-time evaluation in online continual learning: A new hope Y Ghunaim, A Bibi, K Alhamoud, M Alfarra, HA Al Kader Hammoud, ... Conference on Computer Vision and Pattern Recognition (CVPR), 2023 | 102* | 2023 |
| Computationally budgeted continual learning: What does matter? A Prabhu, HA Al Kader Hammoud, PK Dokania, PHS Torr, SN Lim, ... Conference on Computer Vision and Pattern Recognition (CVPR), 3698-3707, 2023 | 102 | 2023 |
| 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 | 101 | 2018 |
| No" zero-shot" without exponential data: Pretraining concept frequency determines multimodal model performance V Udandarao, A Prabhu, A Ghosh, Y Sharma, P Torr, A Bibi, S Albanie, ... Conference on Neural Information Processing (NeurIPS), 2024 | 97 | 2024 |
| 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 | 82 | 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 | 66 | 2016 |
| A sober look at progress in language model reasoning: Pitfalls and paths to reproducibility A Hochlehnert, H Bhatnagar, V Udandarao, S Albanie, A Prabhu, ... Conference on Language Modeling (COLM), 2025 | 55 | 2025 |
| 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 | 50 | 2023 |
| Corrective machine unlearning S Goel, A Prabhu, P Torr, P Kumaraguru, A Sanyal Transactions of Machine Learning Research (TMLR), 2024 | 47 | 2024 |
| Open problems in machine unlearning for ai safety F Barez, T Fu, A Prabhu, S Casper, A Sanyal, A Bibi, A O'Gara, R Kirk, ... arXiv preprint arXiv:2501.04952, 2025 | 44 | 2025 |
| A Practitioner's Guide to Continual Multimodal Pretraining K Roth, V Udandarao, S Dziadzio, A Prabhu, M Cherti, O Vinyals, ... Conference on Neural Information Processing Systems (NeurIPS), 2024 | 31* | 2024 |
| CiteME: Can Language Models Accurately Cite Scientific Claims? O Press, A Hochlehnert, A Prabhu, V Udandarao, O Press, M Bethge Conference on Neural Information Processing Systems (NeurIPS), 2024 | 29 | 2024 |
| 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 | 29 | 2021 |
| 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 International Conference on Computer Vision (ICCV), 2023 | 24* | 2023 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
