Search Results - Izmailov, Pavel

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  1. 1
    Can a Confident Prior Replace a Cold Posterior?

    Can a Confident Prior Replace a Cold Posterior? by Martin, Marek, Brooks, Paige, Izmailov, Pavel

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  2. 2
    Bayesian Deep Learning and a Probabilistic Perspective of Generalization

    Bayesian Deep Learning and a Probabilistic Perspective of Generalization by Andrew Gordon Wilson, Izmailov, Pavel

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  3. 3
    Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

    Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations by Kirichenko, Polina, Izmailov, Pavel, Andrew Gordon Wilson

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  4. 4
    Simple and Fast Group Robustness by Automatic Feature Reweighting

    Simple and Fast Group Robustness by Automatic Feature Reweighting by Qiu, Shikai, Potapczynski, Andres, Izmailov, Pavel, Andrew Gordon Wilson

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  5. 5
    On Feature Learning in the Presence of Spurious Correlations

    On Feature Learning in the Presence of Spurious Correlations by Izmailov, Pavel, Kirichenko, Polina, Gruver, Nate, Andrew Gordon Wilson

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  6. 6
    On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

    On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification by Kapoor, Sanyam, Maddox, Wesley J, Izmailov, Pavel, Andrew Gordon Wilson

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  7. 7
    Why Normalizing Flows Fail to Detect Out-of-Distribution Data

    Why Normalizing Flows Fail to Detect Out-of-Distribution Data by Kirichenko, Polina, Izmailov, Pavel, Andrew Gordon Wilson

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  8. 8
    Faster variational inducing input Gaussian process classification

    Faster variational inducing input Gaussian process classification by Izmailov, Pavel, Kropotov, Dmitry

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  9. 9
    Dangers of Bayesian Model Averaging under Covariate Shift

    Dangers of Bayesian Model Averaging under Covariate Shift by Izmailov, Pavel, Nicholson, Patrick, Lotfi, Sanae, Andrew Gordon Wilson

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  10. 10
    Bayesian Model Selection, the Marginal Likelihood, and Generalization

    Bayesian Model Selection, the Marginal Likelihood, and Generalization by Lotfi, Sanae, Izmailov, Pavel, Benton, Gregory, Goldblum, Micah, Andrew Gordon Wilson

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  11. 11
    What Are Bayesian Neural Network Posteriors Really Like?

    What Are Bayesian Neural Network Posteriors Really Like? by Izmailov, Pavel, Vikram, Sharad, Hoffman, Matthew D, Andrew Gordon Wilson

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  12. 12
    Learning Invariances in Neural Networks

    Learning Invariances in Neural Networks by Benton, Gregory, Finzi, Marc, Izmailov, Pavel, Andrew Gordon Wilson

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  13. 13
    Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

    Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data by Finzi, Marc, Stanton, Samuel, Izmailov, Pavel, Andrew Gordon Wilson

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  14. 14
    FlexiViT: One Model for All Patch Sizes

    FlexiViT: One Model for All Patch Sizes by Beyer, Lucas, Izmailov, Pavel, Kolesnikov, Alexander, Caron, Mathilde, Kornblith, Simon, Zhai, Xiaohua, Minderer, Matthias, Tschannen, Michael, Ibrahim Alabdulmohsin, Pavetic, Filip

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  15. 15
    Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

    Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision by Burns, Collin, Izmailov, Pavel, Kirchner, Jan Hendrik, Baker, Bowen, Gao, Leo, Aschenbrenner, Leopold, Chen, Yining, Ecoffet, Adrien, Joglekar, Manas, Leike, Jan, Sutskever, Ilya, Wu, Jeff

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  16. 16
    Tensor Train decomposition on TensorFlow (T3F)

    Tensor Train decomposition on TensorFlow (T3F) by Novikov, Alexander, Izmailov, Pavel, Khrulkov, Valentin, Figurnov, Michael, Oseledets, Ivan

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  17. 17
    Does Knowledge Distillation Really Work?

    Does Knowledge Distillation Really Work? by Stanton, Samuel, Izmailov, Pavel, Kirichenko, Polina, Alemi, Alexander A, Andrew Gordon Wilson

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  18. 18
    Unsupervised learning of two-component nematicity from STM data on magic angle bilayer graphene

    Unsupervised learning of two-component nematicity from STM data on magic angle bilayer graphene by Taranto, William, Lederer, Samuel, Choi, Youngjoon, Izmailov, Pavel, Andrew Gordon Wilson, Nadj-Perge, Stevan, Kim, Eun-Ah

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  19. 19
    Semi-Supervised Learning with Normalizing Flows

    Semi-Supervised Learning with Normalizing Flows by Izmailov, Pavel, Kirichenko, Polina, Finzi, Marc, Andrew Gordon Wilson

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  20. 20
    Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

    Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition by Izmailov, Pavel, Novikov, Alexander, Kropotov, Dmitry

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