Organizations: NIPS/ICML/ICLR Reviewer, NIPS Blackbox Learning Workshop organizer
PhD Candidate
Massachusetts Institute of Technology, Cambridge
Organizations: NIPS/ICML/ICLR Reviewer, NIPS Blackbox Learning Workshop organizer
Massachusetts Institute of Technology, Cambridge
Google Deepmind
Purdue University
Deep Successor Reinforcement Learning
Tejas Kulkarni*, Ardavan Saeedi*, Simanta Gautam, Sam Gershman
arXiv:1606.02396 [paper] [code] * – Equal contribution
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas Kulkarni*, Karthik Narasimhan*, Ardavan Saeedi, Joshua Tenenbaum
ICML 2016, Workshop on Temporal Abstractions in RL. (Oral Talk) [paper] * – Equal contribution
Understanding Visual Concepts with Continuation Learning
William Whitney, Michael Chang, Tejas Kulkarni, Joshua Tenenbaum
ICLR 2016 Workshop [paper] [project webpage]
Deep Convolutional Inverse Graphics Network
Tejas Kulkarni*, William Whitney*, Pushmeet Kohli, Joshua Tenenbaum
NIPS 2015 (Oral Presentation) [paper] [project webpage] *-Equal contribution
Picture:a probabilistic programming language for scene perception
Tejas Kulkarni*, Pushmeet Kohli, Joshua Tenenbaum, Vikash Mansinghka
CVPR 2015 (Best Paper Hon’ Mention Award) [paper] [project webpage] [Press: MIT News, Phys.org, PCWorld, KurzweilAI, Motherboard]
Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan*, Tejas Kulkarni*, Regina Barzilay
EMNLP 2015 (Best Paper Hon’ Mention Award) [paper] [code] [Press: MIT News] *-Equal contribution
Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and comparison with neural representations
Ilker Yildrim, Tejas Kulkarni, Winrich Friewald, Joshua Tenenbaum
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society 2015 [paper]
Variational Particle Approximations
Ardavan Saeedi*, Tejas Kulkarni*, Vikash Mansinghka, Sam Gershman
NIPS Workshop on Advances in Variational Inference 2015 (Oral Presentation) [paper] *-Equal contribution
Deep Generative Vision as Approximate Bayesian Computation
Tejas Kulkarni, Ilker Yildirim, Pushmeet Kohli, Winrich Freiwald, Joshua Tenenbaum
NIPS Workshop on Approximate Bayesian Computation 2014 [paper]
Approximate bayesian image interpretation using generative probabilistic graphics programs
Vikash Mansinghka*, Tejas Kulkarni*, Yura Perov, Joshua Tenenbaum
NIPS 2013 (Oral Presentation) [paper] *-Equal contribution