| Deep convolutional inverse graphics network TD Kulkarni, W Whitney, P Kohli, JB Tenenbaum arXiv preprint arXiv:1503.03167, 2015 | 850 | 2015 |
| Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, K Narasimhan, A Saeedi, J Tenenbaum Advances in neural information processing systems 29, 3675-3683, 2016 | 746 | 2016 |
| Language understanding for text-based games using deep reinforcement learning K Narasimhan, T Kulkarni, R Barzilay arXiv preprint arXiv:1506.08941, 2015 | 313 | 2015 |
| Picture: a probabilistic programming language for scene perception TD Kulkarni, P Kohli, JB Tenenbaum, VK Mansinghka Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 190 | 2015 |
| Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks A Arsalan Soltani, H Huang, J Wu, TD Kulkarni, JB Tenenbaum Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 157 | 2017 |
| Deep successor reinforcement learning TD Kulkarni, A Saeedi, S Gautam, SJ Gershman arXiv preprint arXiv:1606.02396, 2016 | 147 | 2016 |
| Synthesizing programs for images using reinforced adversarial learning Y Ganin, T Kulkarni, I Babuschkin, SMA Eslami, O Vinyals International Conference on Machine Learning, 1666-1675, 2018 | 130 | 2018 |
| Approximate bayesian image interpretation using generative probabilistic graphics programs VK Mansinghka, TD Kulkarni, YN Perov, J Tenenbaum Advances in Neural Information Processing Systems 26, 1520-1528, 2013 | 102 | 2013 |
| Unsupervised learning of object keypoints for perception and control T Kulkarni, A Gupta, C Ionescu, S Borgeaud, M Reynolds, A Zisserman, ... arXiv preprint arXiv:1906.11883, 2019 | 71 | 2019 |
| Unsupervised control through non-parametric discriminative rewards D Warde-Farley, T Van de Wiele, T Kulkarni, C Ionescu, S Hansen, ... arXiv preprint arXiv:1811.11359, 2018 | 69 | 2018 |
| Self-supervised intrinsic image decomposition M Janner, J Wu, TD Kulkarni, I Yildirim, JB Tenenbaum arXiv preprint arXiv:1711.03678, 2017 | 68 | 2017 |
| Learning to perform physics experiments via deep reinforcement learning M Denil, P Agrawal, TD Kulkarni, T Erez, P Battaglia, N De Freitas arXiv preprint arXiv:1611.01843, 2016 | 66 | 2016 |
| Use of association of an object detected in an image to obtain information to display to a user TD Kulkarni, B Liu, AB Nandwani, JE Taseski, BJ Yule, D Kaleas, ... US Patent App. 13/549,339, 2013 | 58 | 2013 |
| Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations I Yildirim, TD Kulkarni, WA Freiwald, JB Tenenbaum Annual conference of the cognitive science society 1 (2), 2015 | 49 | 2015 |
| Understanding visual concepts with continuation learning WF Whitney, M Chang, T Kulkarni, JB Tenenbaum arXiv preprint arXiv:1602.06822, 2016 | 43 | 2016 |
| Variational particle approximations A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman The Journal of Machine Learning Research 18 (1), 2328-2356, 2017 | 27 | 2017 |
| Inverse graphics with probabilistic cad models TD Kulkarni, VK Mansinghka, P Kohli, JB Tenenbaum arXiv preprint arXiv:1407.1339, 2014 | 23 | 2014 |
| Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and comparison with neural representations I Yildirim, TD Kulkarni, WA Freiwald, JB Tenenbaum Thirty-Seventh Annual Conference of the Cognitive Science Society 4, 2015 | 21 | 2015 |
| Deep Generative Vision as Approximate Bayesian Computation TD Kulkarni, I Yildirim, P Kohli, WA Freiwald, JB Tenenbaum Neural Information Processing Systems, 2014 | 19 | 2014 |
| Unsupervised doodling and painting with improved spiral JFJ Mellor, E Park, Y Ganin, I Babuschkin, T Kulkarni, D Rosenbaum, ... arXiv preprint arXiv:1910.01007, 2019 | 17 | 2019 |
Joshua B. TenenbaumMITVerified email at mit.edu
