Tejas D. Kulkarni

发表

Deep Convolutional Inverse Graphics Network

Joshua B. Tenenbaum, Pushmeet Kohli, William F. Whitney, 2015, NIPS.

Picture: A probabilistic programming language for scene perception

Joshua B. Tenenbaum, Pushmeet Kohli, Vikash K. Mansinghka, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder

Jiajun Wu, Josh Tenenbaum, Tejas D. Kulkarni, 2017, NIPS 2017.

Understanding Visual Concepts with Continuation Learning pdf

Joshua B. Tenenbaum, William F. Whitney, Tejas D. Kulkarni, 2016, ArXiv.

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Joshua B. Tenenbaum, Ardavan Saeedi, Karthik Narasimhan, 2016, NIPS.

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

Joshua B. Tenenbaum, Yura N. Perov, Vikash K. Mansinghka, 2013, NIPS.

Inverse Graphics with Probabilistic CAD Models pdf

Joshua B. Tenenbaum, Pushmeet Kohli, Vikash K. Mansinghka, 2014, ArXiv.

Language Understanding for Text-based Games using Deep Reinforcement Learning

Regina Barzilay, Karthik Narasimhan, Tejas D. Kulkarni, 2015, EMNLP.

Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning

Tejas D. Kulkarni, Catalin Ionescu, aaron van de oord, 2018 .

Deep Successor Reinforcement Learning pdf

Samuel Gershman, Ardavan Saeedi, Tejas D. Kulkarni, 2016, ArXiv.

Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations

Joshua B. Tenenbaum, Tejas D. Kulkarni, Ilker Yildirim, 2015, Annual Meeting of the Cognitive Science Society.

Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks

Jiajun Wu, Joshua B. Tenenbaum, Haibin Huang, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Self-Supervised Intrinsic Image Decomposition

Jiajun Wu, Joshua B. Tenenbaum, Tejas D. Kulkarni, 2017, NIPS.

Learning to Perform Physics Experiments via Deep Reinforcement Learning pdf

Misha Denil, Nando de Freitas, Tom Erez, 2016, ICLR.

Applying mobile device soft keyboards to collaborative multitouch tabletop displays: design and evaluation

Niklas Elmqvist, Sungahn Ko, KyungTae Kim, 2011, ITS '11.

Variational Particle Approximations pdf

Ardavan Saeedi, Vikash K. Mansinghka, Tejas D. Kulkarni, 2014, J. Mach. Learn. Res..

Deep Generative Vision as Approximate Bayesian Computation

Tejas D. Kulkarni, J. Tenenbaum, Pushmeet Kohli, 2014 .

Explaining monkey face patch system as deep inverse graphics

Tejas D. Kulkarni, J. Tenenbaum, W. Freiwald, 2014 .

Unsupervised Control Through Non-Parametric Discriminative Rewards pdf

David Warde-Farley, Volodymyr Mnih, Tejas D. Kulkarni, 2018, ICLR.

Unsupervised Learning of Object Keypoints for Perception and Control

Ankush Gupta, Andrew Zisserman, Volodymyr Mnih, 2019, NeurIPS.

Unsupervised Doodling and Painting with Improved SPIRAL pdf

Eunbyung Park, S. M. Ali Eslami, Oriol Vinyals, 2019, ArXiv.

Unsupervised Control Through Non-Parametric Discriminative Rewards

Tejas D. Kulkarni, Volodymyr Mnih, T. Wiele, 2018, ICLR.

Deep Reinforcement Learning with Temporal Abstraction and Intrinsic Motivation

Tejas D. Kulkarni, J. Tenenbaum, Karthik Narasimhan, 2016 .

DEEP REINFORCEMENT LEARNING

Tejas D. Kulkarni, T. Erez, N. D. Freitas, 2016 .

Answering Range Queries Under Local Differential Privacy

Tejas D. Kulkarni, Graham Cormode, D. Srivastava, 2018, SIGMOD Conference.

Synthesizing Programs for Images using Reinforced Adversarial Learning pdf

Oriol Vinyals, S. M. Ali Eslami, Tejas D. Kulkarni, 2018, ICML.

Practical Differentially Private Hyperparameter Tuning with Subsampling

Tejas D. Kulkarni, A. Koskela, 2023, arXiv.org.

Representation Matters: Improving Perception and Exploration for Robotics pdf

Ankush Gupta, Martin A. Riedmiller, Thomas Lampe, 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Privacy at Scale: Local Dierential Privacy in Practice

Ninghui Li, Somesh Jha, Divesh Srivastava, 2018 .

Practical Differentially Private Hyperparameter Tuning with Subsampling

Tejas D. Kulkarni, A. Koskela, 2023, ArXiv.

Answering Range Queries Under Local Differential Privacy

Divesh Srivastava, Graham Cormode, Tejas D. Kulkarni, 2018, Proc. VLDB Endow..

Locally Differentially Private Bayesian Inference pdf

Antti Honkela, Samuel Kaski, Joonas Jälkö, 2021, ArXiv.

Differentially Private Bayesian Inference for Generalized Linear Models

Tejas D. Kulkarni, Samuel Kaski, A. Honkela, 2020, ICML.

Marginal Release Under Local Differential Privacy

Divesh Srivastava, Graham Cormode, Tejas D. Kulkarni, 2017, SIGMOD Conference.

Private Protocols for U-Statistics in the Local Model and Beyond

Tejas D. Kulkarni, Adrià Gascón, A. Bellet, 2019, AISTATS.

Privacy at Scale: Local Differential Privacy in Practice

Tejas D. Kulkarni, Graham Cormode, S. Jha, 2018, SIGMOD Conference.

Leader Election in Sparse Dynamic Networks with Churn

John Augustine, Sumathi Sivasubramaniam, Tejas D. Kulkarni, 2015, 2015 IEEE International Parallel and Distributed Processing Symposium.

Constrained Private Mechanisms for Count Data

Divesh Srivastava, Graham Cormode, Tejas D. Kulkarni, 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

Constrained Differential Privacy for Count Data pdf

Divesh Srivastava, Graham Cormode, Tejas D. Kulkarni, 2017, ICDE 2018.