| Gdumb: A simple approach that questions our progress in continual learning A Prabhu, PHS Torr, PK Dokania European conference on computer vision, 524-540, 2020 | 541 | 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 | 163 | 2016 |
| Simple unsupervised multi-object tracking S Karthik, A Prabhu, V Gandhi arXiv preprint arXiv:2006.02609, 2020 | 92 | 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 |
| 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 | 63 | 2016 |
| Sampling bias in deep active classification: An empirical study A Prabhu, C Dognin, M Singh arXiv preprint arXiv:1909.09389, 2019 | 57 | 2019 |
| Towards sub-word level compositions for sentiment analysis of hindi-english code mixed text A Prabhu, A Joshi, M Shrivastava, V Varma arXiv preprint arXiv:1611.00472, 2016 | 46 | 2016 |
| 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 arXiv preprint arXiv:2104.00795, 2021 | 13 | 2021 |
| Distilling large language models into tiny and effective students using pqrnn AS Prabhu Kaliamoorthi, E Li, M Johnson CoRR, abs/2101.08890, 2021 | 12 | 2021 |
| Evaluating Inexact Unlearning Requires Revisiting Forgetting S Goel, A Prabhu, P Kumaraguru arXiv preprint arXiv:2201.06640, 2022 | 8 | 2022 |
| The magnetic fine structure of the Sun’s polar region as revealed by Sunrise A Prabhu, A Lagg, J Hirzberger, SK Solanki Astronomy & Astrophysics 644, A86, 2020 | 5 | 2020 |
| Helicity proxies from linear polarisation of solar active regions A Prabhu, A Brandenburg, MJ Käpylä, A Lagg Astronomy & Astrophysics 641, A46, 2020 | 5 | 2020 |
| Inferring magnetic helicity spectrum in spherical domains: Method and example applications AP Prabhu, NK Singh, MJ Käpylä, A Lagg Astronomy & Astrophysics 654, A3, 2021 | 4 | 2021 |
| 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 | 4 | 2021 |
| STQ-Nets: Unifying Network Binarization and Structured Pruning SA Munagala, A Prabhu, AM Namboodiri BMVC, 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 |
| A flexible approach for securing MANETs A Prabhu, R Dhanaraj International Conference on Recent Advances and Innovations in Engineering …, 2014 | 3 | 2014 |
| Record of a hyperparasitoid on Pseudogonatopus nudus Perkins (Dryinidae: Chrysidoidea) parasitizing Nilaparvata lugens (Stål) from Asia. S Manickavasagam, A Prabhu, R Kanagarajan International Rice Research Notes 31 (1), 24-25, 2006 | 3 | 2006 |
| 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 |
