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05/28/2021
Latent Space Exploration Using Generative Kernel PCA
Kernel PCA is a powerful feature extractor which recently has seen a ref...
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05/28/2021
Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse s...
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04/06/2021
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Commonly, machine learning models minimize an empirical expectation. As ...
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11/13/2020
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Semi-parametric regression models are used in several applications which...
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06/24/2020
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes
By using the framework of Determinantal Point Processes (DPPs), some the...
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06/16/2020
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
Generative Adversarial Networks (GANs) are performant generative methods...
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06/12/2020
Disentangled Representation Learning and Generation with Manifold Optimization
Disentanglement is an enjoyable property in representation learning whic...
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02/20/2020
Diversity sampling is an implicit regularization for kernel methods
Kernel methods have achieved very good performance on large scale regres...
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02/04/2020
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
In the past decade, interest in generative models has grown tremendously...
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06/19/2019
Generative Restricted Kernel Machines
We propose a novel method for estimating generative models based on the ...
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05/29/2019