91 citations · 113 across the 5 of their papers we have counts for
5 papers
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson +3
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision m…
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Jalil Taghia, Maria Bånkestad, Fredrik Lindsten +1
Like most learning algorithms, the multilayer perceptrons (MLP) is designed to learn a vector of parameters from data. However, in certain scenarios we are interested in learning s…
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and ra…
Coupling of Particle Filters
Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön
Particle filters provide Monte Carlo approximations of intractable quantities such as point-wise evaluations of the likelihood in state space models. In many scenarios, the interes…
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-i…