16 citations · 73 across the 12 of their papers we have counts for
14 papers
Is Supervised Learning Really That Different from Unsupervised?
Oskar Allerbo, Thomas B. Schön
We demonstrate how supervised learning can be decomposed into a two-stage procedure, where (1) all model parameters are selected in an unsupervised manner, and (2) the outputs y ar…
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Dominik Baumann, Erfaun Noorani, James Price +3
Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the re…
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping
Manon Kok, Arno Solin, Thomas B. Schön
Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic…
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network arch…
Invertible Kernel PCA with Random Fourier Features
Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström +1
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to…
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…