| Self-paced learning for latent variable models M Kumar, B Packer, D Koller Advances in neural information processing systems 23, 2010 | 1744 | 2010 |
| Objcut: Efficient segmentation using top-down and bottom-up cues MP Kumar, PHS Torr, A Zisserman Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (3), 530-545, 2010 | 659* | 2010 |
| A unified view of piecewise linear neural network verification RR Bunel, I Turkaslan, P Torr, P Kohli, PK Mudigonda Advances in neural information processing systems 31, 2018 | 505* | 2018 |
| P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions P Kohli, MP Kumar, PHS Torr Pattern Analysis and Machine Intelligence, IEEE Transactions on 31 (9), 1645 …, 2009 | 473* | 2009 |
| Mathematical discoveries from program search with large language models B Romera-Paredes, M Barekatain, A Novikov, M Balog, MP Kumar, ... Nature 625 (7995), 468-475, 2024 | 360 | 2024 |
| Learning layered motion segmentations of video M Pawan Kumar, PHS Torr, A Zisserman International Journal of Computer Vision 76, 301-319, 2008 | 254 | 2008 |
| Branch and bound for piecewise linear neural network verification R Bunel, J Lu, I Turkaslan, PHS Torr, P Kohli, MP Kumar Journal of Machine Learning Research 21 (42), 1-39, 2020 | 239 | 2020 |
| Efficiently selecting regions for scene understanding MP Kumar, D Koller 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 187 | 2010 |
| Hybrid models for learning to branch P Gupta, M Gasse, E Khalil, P Mudigonda, A Lodi, Y Bengio Advances in neural information processing systems 33, 18087-18097, 2020 | 157 | 2020 |
| Smooth loss functions for deep top-k classification L Berrada, A Zisserman, MP Kumar arXiv preprint arXiv:1802.07595, 2018 | 142 | 2018 |
| Parameter estimation and energy minimization for region-based semantic segmentation MP Kumar, H Turki, D Preston, D Koller IEEE transactions on pattern analysis and machine intelligence 37 (7), 1373-1386, 2014 | 134* | 2014 |
| Efficient discriminative learning of parts-based models MP Kumar, A Zisserman, PHS Torr 2009 IEEE 12th International Conference on Computer Vision, 552-559, 2009 | 123 | 2009 |
| An analysis of convex relaxations for MAP estimation of discrete MRFs MP Kumar, V Kolmogorov, PHS Torr The Journal of Machine Learning Research 10, 71-106, 2009 | 121* | 2009 |
| Dissimilarity coefficient based weakly supervised object detection A Arun, CV Jawahar, MP Kumar Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 108 | 2019 |
| A statistical approach to assessing neural network robustness S Webb, T Rainforth, YW Teh, MP Kumar arXiv preprint arXiv:1811.07209, 2018 | 108 | 2018 |
| Extending pictorial structures for object recognition MP Kumar, PHS Torr, A Zisserman British Machine Vision Association, 2004 | 105 | 2004 |
| In defense of the unitary scalarization for deep multi-task learning V Kurin, A De Palma, I Kostrikov, S Whiteson, PK Mudigonda Advances in Neural Information Processing Systems 35, 12169-12183, 2022 | 78 | 2022 |
| Improved moves for truncated convex models MP Kumar, O Veksler, PHS Torr The Journal of Machine Learning Research 12, 31-67, 2011 | 78* | 2011 |
| Weakly supervised instance segmentation by learning annotation consistent instances A Arun, CV Jawahar, MP Kumar European Conference on Computer Vision, 254-270, 2020 | 75 | 2020 |
| Neural network branching for neural network verification J Lu, MP Kumar arXiv preprint arXiv:1912.01329, 2019 | 74 | 2019 |
Philip TorrProfessor, University of Oxfordایمیل تأیید شده در eng.ox.ac.uk