| 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 | 297 | 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 | 159* | 2016 |
| Simple unsupervised multi-object tracking S Karthik, A Prabhu, V Gandhi arXiv preprint arXiv:2006.02609, 2020 | 69 | 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 | 67 | 2018 |
| 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 | 55 | 2016 |
| 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 | 40 | 2019 |
| 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 | 9 | 2021 |
| 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 | 4* | 2023 |
| Evaluating inexact unlearning requires revisiting forgetting S Goel, A Prabhu, P Kumaraguru arXiv preprint arXiv:2201.06640, 2022 | 3 | 2022 |
| Machine Learning Systems and Methods for Evaluating Sampling Bias in Deep Active Classification A Prabhu, C Dognin, MK Singh US Patent App. 16/919,898, 2021 | 3 | 2021 |
| 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 | 2 | 2023 |
| 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 | 2 | 2023 |
| STQ-Nets: Unifying Network Binarization and Structured Pruning SA Munagala, A Prabhu, A Namboodiri Proceedings of the British Machine Vision Conference (BMVC) 2020, 2020 | 2 | 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 | 2 | 2018 |
| Learning clustered sub-spaces for sketch-based image retrieval K Ghosal, A Prabhu, R Dasgupta, AM Namboodiri 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 599-603, 2015 | 1 | 2015 |
| Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? HAAK Hammoud, A Prabhu, SN Lim, PHS Torr, A Bibi, B Ghanem arXiv preprint arXiv:2305.09275, 2023 | | 2023 |
| CLActive: Episodic Memories for Rapid Active Learning SA Munagala, S Subramanian, S Karthik, A Prabhu, A Namboodiri Conference on Lifelong Learning Agents, 430-440, 2022 | | 2022 |
| " You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets A Prabhu, R Dasgupta, A Sankaran, S Tamilselvam, S Mani arXiv preprint arXiv:1911.11433, 2019 | | 2019 |
| Exploring Binarization and Pruning of Convolutional Neural Networks A Prabhu International Institute of Information Technology Hyderabad, 2019 | | 2019 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
