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Found 14 papers, 1 papers with code
Latent Space Exploration Using Generative Kernel PCA
This connection has led to insights on how to use kernel PCA in a generative procedure, called generative kernel PCA.
Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search.
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution.
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy.
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
Outlier detection in non-elliptical data by kernel MRCD
The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting a robust covariance matrix to the data.
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes
By using the framework of Determinantal Point Processes (DPPs), some theoretical results concerning the interplay between diversity and regularization can be obtained.
The Bures Metric for Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
Disentangled Representation Learning and Generation with Manifold Optimization
Our analysis shows that such a construction promotes disentanglement by matching the principal directions in the latent space with the directions of orthogonal variation in data space.
Diversity sampling is an implicit regularization for kernel methods
The Nystr\"om approximation -- based on a subset of landmarks -- gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization.
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data.
Generative Restricted Kernel Machines
We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM.
Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning
This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM.
Nyström landmark sampling and regularized Christoffel functions
In this context, we propose a deterministic and a randomized adaptive algorithm for selecting landmark points within a training data set.
