2 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…
cs.LG2020
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine…