Showing 1 - 20 results of 149 for search ' Neural Information Processing Systems Foundation', query time: 0.11s Refine Results
  1. 1
    Coresets for k-Segmentation of Streaming Data

    Coresets for k-Segmentation of Streaming Data by Rosman, Guy, Volkov, Mikhail, Feldman, Dan, Fisher III, John W., Rus, Daniela L.

    Published 2016
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  2. 2
    Learning Mixed Multinomial Logit Model from Ordinal Data

    Learning Mixed Multinomial Logit Model from Ordinal Data by Oh, Sewoong, Shah, Devavrat

    Published 2016
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  3. 3
    Structure Learning of Antiferromagnetic Ising Models

    Structure Learning of Antiferromagnetic Ising Models by Bresler, Guy, Gamarnik, David, Shah, Devavrat

    Published 2016
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  4. 4
    Hardness of Parameter Estimation in Graphical Models

    Hardness of Parameter Estimation in Graphical Models by Bresler, Guy, Gamarnik, David, Shah, Devavrat

    Published 2016
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  5. 5
    Feature Selection for SVMs

    Feature Selection for SVMs by Poggio, Tomaso A., Weston, Jason, Mukherjee, Sayan, Pontil, Massimiliano, Chapelle, Olivier, Vapnik, Vladimir

    Published 2016
    “…Neural Information Processing Systems Foundation…”
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  6. 6
    Streaming, distributed variational inference for Bayesian nonparametrics

    Streaming, distributed variational inference for Bayesian nonparametrics by Campbell, Trevor David, Straub, Julian, Fisher, John W, How, Jonathan P

    Published 2016
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  7. 7
    Linear response methods for accurate covariance estimates from mean field variational bayes

    Linear response methods for accurate covariance estimates from mean field variational bayes by Giordano, Ryan, Jordan, Michael, Broderick, Tamara A

    Published 2017
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  8. 8
    Optimal testing for properties of distributions

    Optimal testing for properties of distributions by Acharya, Jayadev, Daskalakis, Konstantinos, Kamath, Gautam Chetan

    Published 2017
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  9. 9
    Sampling for Bayesian program learning

    Sampling for Bayesian program learning by Ellis, Kevin M., Solar Lezama, Armando, Tenenbaum, Joshua B

    Published 2017
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  10. 10
    Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model

    Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model by Alvarez, George, Vul, Edward, Frank, Michael C., Tenenbaum, Joshua B

    Published 2017
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  11. 11
    Probing the compositionality of intuitive functions

    Probing the compositionality of intuitive functions by Schulz, Eric, Duvenaud, David K., Speekenbrink, Maarten, Gershman, Samuel J., Tenenbaum, Joshua B

    Published 2017
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  12. 12
    Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling

    Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling by Wu, Jiajun, Zhang, Chengkai, Xue, Tianfan, Freeman, William T., Tenenbaum, Joshua B.

    Published 2017
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  13. 13
    Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation

    Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation by Kulkarni, Tejas D., Narasimhan, Karthik Rajagopal, Saeedi, Ardavan, Tenenbaum, Joshua B

    Published 2017
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  14. 14
    Dynamic infinite relational model for time-varying relational data analysis

    Dynamic infinite relational model for time-varying relational data analysis by Ishiguro, Katsuhiko, Iwata, Tomoharu, Ueda, Naonori, Tenenbaum, Joshua B

    Published 2017
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  15. 15
    Learning and using relational theories

    Learning and using relational theories by Kemp, Charles, Goodman, Noah Daniel, Tenenbaum, Joshua B

    Published 2017
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  16. 16
    Perceptual multistability as Markov Chain Monte Carlo inference

    Perceptual multistability as Markov Chain Monte Carlo inference by Gershman, Samuel J., Vul, Edward, Tenenbaum, Joshua B

    Published 2017
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  17. 17
    Modelling relational data using Bayesian clustered tensor factorization

    Modelling relational data using Bayesian clustered tensor factorization by Sutskever, Ilya, Tenenbaum, Joshua B., Salakhutdinov, Ruslan

    Published 2017
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  18. 18
    A Bayesian framework for cross-situational word-learning

    A Bayesian framework for cross-situational word-learning by Goodman, Noah Daniel, Tenenbaum, Joshua B, Frank, Michael C.

    Published 2017
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  19. 19
    Inverse reinforcement learning with locally consistent reward functions

    Inverse reinforcement learning with locally consistent reward functions by Nguyen, Quoc Phong, Low, Bryan Kian Hsiang, Jaillet, Patrick

    Published 2018
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  20. 20
    Where are they looking?

    Where are they looking? by Recasens Continente, Adria, Khosla, Aditya, Carl Vondrick, Torralba, Antonio

    Published 2018
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