38 citations · 61 across the 17 of their papers we have counts for
9 papers · 1 filter
Deep Automodulators
Ari Heljakka, Yuxin Hou, Juho Kannala +1
We introduce a new category of generative autoencoders called automodulators. These networks can faithfully reproduce individual real-world input images like regular autoencoders,…
Gaussian Process Priors for View-Aware Inference
Yuxin Hou, Ari Heljakka, Arno Solin
While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations be…
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, Will Tebbutt +3
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-te…
Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones
Santiago Cortés Reina, Yuxin Hou, Juho Kannala +1
Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking. In specific environments some data streams may be absent, less reliab…
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Arno Solin, Manon Kok
Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification. This paper considers constraining GPs to…
Multi-View Stereo by Temporal Nonparametric Fusion
Yuxin Hou, Juho Kannala, Arno Solin
We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the sc…