1 citations · 2 across the 6 of their papers we have counts for
6 papers
Duality in Multi-View Restricted Kernel Machines
Sonny Achten, Arun Pandey, Hannes De Meulemeester +2
We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in bot…
Multi-view Kernel PCA for Time series Forecasting
Arun Pandey, Hannes De Meulemeester, Bart De Moor +1
In this paper, we propose a kernel principal component analysis model for multi-variate time series forecasting, where the training and prediction schemes are derived from the mult…
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Francesco Tonin, Arun Pandey, Panagiotis Patrinos +1
Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world. Until now, research on energy-based OOD de…
Disentangled Representation Learning and Generation with Manifold Optimization
Arun Pandey, Michael Fanuel, Joachim Schreurs +1
Disentanglement is a useful property in representation learning which increases the interpretability of generative models such as Variational autoencoders (VAE), Generative Adversa…
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded…
Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
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…