1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…