3 papers
cs.LG2021
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…
stat.ML2020
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…
cs.LG2019
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…