Search Results - Amid, Ehsan

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  1. 1
    Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment

    Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment by Talebi, Hossein, Amid, Ehsan, Milanfar, Peyman, Warmuth, Manfred K.

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  2. 2
    An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint

    An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint by Amid, Ehsan, Warmuth, Manfred K.

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  3. 3
    Optimal Transport with Tempered Exponential Measures

    Optimal Transport with Tempered Exponential Measures by Amid, Ehsan, Nielsen, Frank, Nock, Richard, Warmuth, Manfred K.

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  4. 4
    Unsupervised feature extraction for multimedia event detection and ranking using audio content

    Unsupervised feature extraction for multimedia event detection and ranking using audio content by Amid, Ehsan, Mesaros, Annamaria, Palomaki, Kalle J., Laaksonen, Jorma, Kurimo, Mikko

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  5. 5
    Constrained Instance and Class Reweighting for Robust Learning under Label Noise

    Constrained Instance and Class Reweighting for Robust Learning under Label Noise by Kumar, Abhishek, Amid, Ehsan

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  6. 6
    TriMap: Large-scale Dimensionality Reduction Using Triplets

    TriMap: Large-scale Dimensionality Reduction Using Triplets by Amid, Ehsan, Warmuth, Manfred K

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  7. 7
    Boosting with Tempered Exponential Measures

    Boosting with Tempered Exponential Measures by Nock, Richard, Amid, Ehsan, Warmuth, Manfred K

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  8. 8
    Clustering above Exponential Families with Tempered Exponential Measures

    Clustering above Exponential Families with Tempered Exponential Measures by Amid, Ehsan, Nock, Richard, Warmuth, Manfred

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  9. 9
    Noise misleads rotation invariant algorithms on sparse targets

    Noise misleads rotation invariant algorithms on sparse targets by Warmuth, Manfred K, Kotłowski, Wojciech, Jones, Matt, Amid, Ehsan

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  10. 10
    Optimal Transport with Tempered Exponential Measures

    Optimal Transport with Tempered Exponential Measures by Amid, Ehsan, Nielsen, Frank, Nock, Richard, Warmuth, Manfred K

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  11. 11
    The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs

    The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs by Amid, Ehsan, Nielsen, Frank, Nock, Richard, Warmuth, Manfred K

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  12. 12
    LocoProp: Enhancing BackProp via Local Loss Optimization

    LocoProp: Enhancing BackProp via Local Loss Optimization by Amid, Ehsan, Rohan, Anil, Warmuth, Manfred K

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  13. 13
    Reparameterizing Mirror Descent as Gradient Descent

    Reparameterizing Mirror Descent as Gradient Descent by Amid, Ehsan, Warmuth, Manfred K

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  14. 14
    Divergence-Based Motivation for Online EM and Combining Hidden Variable Models

    Divergence-Based Motivation for Online EM and Combining Hidden Variable Models by Amid, Ehsan, Warmuth, Manfred K

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  15. 15
    Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments

    Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments by Aghdam, Sina Rezaei, Amid, Ehsan, Furdek, Marija, Graell i Amat, Alexandre

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  16. 16
    An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint

    An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint by Amid, Ehsan, Warmuth, Manfred K

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  17. 17
    Tempered Calculus for ML: Application to Hyperbolic Model Embedding

    Tempered Calculus for ML: Application to Hyperbolic Model Embedding by Nock, Richard, Amid, Ehsan, Nielsen, Frank, Soen, Alexander, Warmuth, Manfred K

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  18. 18
    Layerwise Bregman Representation Learning with Applications to Knowledge Distillation

    Layerwise Bregman Representation Learning with Applications to Knowledge Distillation by Amid, Ehsan, Rohan, Anil, Fifty, Christopher, Warmuth, Manfred K

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  19. 19
    Context-Aware Meta-Learning

    Context-Aware Meta-Learning by Fifty, Christopher, Duan, Dennis, Junkins, Ronald G, Amid, Ehsan, Leskovec, Jure, Re, Christopher, Thrun, Sebastian

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  20. 20
    A case where a spindly two-layer linear network whips any neural network with a fully connected input layer

    A case where a spindly two-layer linear network whips any neural network with a fully connected input layer by Warmuth, Manfred K, Kotłowski, Wojciech, Amid, Ehsan

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