Tejas Kulkarni
Cited by
Cited by
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, 2016
Deep convolutional inverse graphics network
TD Kulkarni, WF Whitney, P Kohli, J Tenenbaum
Advances in neural information processing systems 28, 2015
Language understanding for text-based games using deep reinforcement learning
K Narasimhan, T Kulkarni, R Barzilay
arXiv preprint arXiv:1506.08941, 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
Deep successor reinforcement learning
TD Kulkarni, A Saeedi, S Gautam, SJ Gershman
arXiv preprint arXiv:1606.02396, 2016
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
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
Unsupervised learning of object keypoints for perception and control
TD Kulkarni, A Gupta, C Ionescu, S Borgeaud, M Reynolds, A Zisserman, ...
Advances in neural information processing systems 32, 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
Approximate bayesian image interpretation using generative probabilistic graphics programs
VK Mansinghka, TD Kulkarni, YN Perov, J Tenenbaum
Advances in Neural Information Processing Systems 26, 2013
Self-supervised intrinsic image decomposition
M Janner, J Wu, TD Kulkarni, I Yildirim, J Tenenbaum
Advances in neural information processing systems 30, 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
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
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
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
Understanding visual concepts with continuation learning
WF Whitney, M Chang, T Kulkarni, JB Tenenbaum
arXiv preprint arXiv:1602.06822, 2016
Variational particle approximations
A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman
The Journal of Machine Learning Research 18 (1), 2328-2356, 2017
Differentially private Bayesian inference for generalized linear models
T Kulkarni, J Jälkö, A Koskela, S Kaski, A Honkela
International Conference on Machine Learning, 5838-5849, 2021
Inverse graphics with probabilistic cad models
TD Kulkarni, VK Mansinghka, P Kohli, JB Tenenbaum
arXiv preprint arXiv:1407.1339, 2014
Deep Generative Vision as Approximate Bayesian Computation
TD Kulkarni, I Yildirim, P Kohli, WA Freiwald, JB Tenenbaum
Neural Information Processing Systems, 2014
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