ERD

A Statistical Learning Approach to Modal Regression

Project: E-DUALITY

Updated at: 27-04-2024

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Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

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Updated at: 27-04-2024

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Parallelized Tensor Train Learning of Polynomial Classifiers

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Updated at: 27-04-2024

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Generalized support vector regression: Duality and tensor-kernel representation

Project: E-DUALITY

Updated at: 27-04-2024

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Robust classification of graph-based data

Project: E-DUALITY

Updated at: 27-04-2024

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Deformed Laplacians and spectral ranking in directed networks

Project: E-DUALITY

Updated at: 27-04-2024

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Multi-View Kernel Spectral Clustering

Project: E-DUALITY

Updated at: 27-04-2024

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Sparse Kernel Regression with Coefficient-based ℓq− regularization

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

A novel neural grey system model with Bayesian regularization and its applications

Project: E-DUALITY

Updated at: 27-04-2024

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Piecewise Linear Neural Networks and Deep Learning

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Updated at: 27-04-2024

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Kernel regression in high dimensions: Refined analysis beyond double descent

Project: E-DUALITY

Updated at: 27-04-2024

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Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts

Project: E-DUALITY

Updated at: 27-04-2024

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A Theoretical Framework for Target Propagation

Project: E-DUALITY

Updated at: 27-04-2024

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Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

The Bures Metric for Generative Adversarial Networks

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Boosting Co-teaching with Compression Regularization for Label Noise

Project: E-DUALITY

Updated at: 27-04-2024

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Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

Project: E-DUALITY

Updated at: 27-04-2024

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Wasserstein Exponential Kernels

Project: E-DUALITY

Updated at: 27-04-2024

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Recurrent Restricted Kernel Machines for Time-series Forecasting

Project: E-DUALITY

Updated at: 27-04-2024

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Outlier detection in non-elliptical data by kernel MRCD

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

Project: E-DUALITY

Updated at: 27-04-2024

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Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

Towards Deterministic Diverse Subset Sampling

Project: E-DUALITY

Updated at: 27-04-2024

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Project acronym E-DUALITY

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels

Project: E-DUALITY

Updated at: 27-04-2024

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Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Project: E-DUALITY

Updated at: 27-04-2024

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Nyström landmark sampling and regularized Christoffel functions

Project: E-DUALITY

Updated at: 27-04-2024

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Resources

Selected filters:

A Statistical Learning Approach to Modal Regression

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

Parallelized Tensor Train Learning of Polynomial Classifiers

Generalized support vector regression: Duality and tensor-kernel representation

Robust classification of graph-based data

Deformed Laplacians and spectral ranking in directed networks

Multi-View Kernel Spectral Clustering

Sparse Kernel Regression with Coefficient-based ℓq− regularization

A novel neural grey system model with Bayesian regularization and its applications

Piecewise Linear Neural Networks and Deep Learning

Kernel regression in high dimensions: Refined analysis beyond double descent

Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts

A Theoretical Framework for Target Propagation

Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer

Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes

The Bures Metric for Generative Adversarial Networks

Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

Boosting Co-teaching with Compression Regularization for Label Noise

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

Wasserstein Exponential Kernels

Recurrent Restricted Kernel Machines for Time-series Forecasting

Outlier detection in non-elliptical data by kernel MRCD

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent

Towards Deterministic Diverse Subset Sampling

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Nyström landmark sampling and regularized Christoffel functions