139 citations · 649 across the 28 of their papers we have counts for
7 papers · 1 filter
Uncertainty quantification in neural network classifiers -- a local linear approach
Magnus Malmström, Isaac Skog, Daniel Axehill +1
Classifiers based on neural networks (NN) often lack a measure of uncertainty in the predicted class. We propose a method to estimate the probability mass function (PMF) of the dif…
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a…
Learning Proposals for Practical Energy-Based Regression
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression. However, energy…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Energy-Based Models for Deep Probabilistic Regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat +1
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one parti…
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictio…
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family
Tohid Ardeshiri, Umut Orguner, Fredrik Gustafsson
In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised f…