91 citations · 173 across the 12 of their papers we have counts for
6 papers · 1 filter
Markovian Score Climbing: Variational Inference with KL(p||q)
Christian A. Naesseth, Fredrik Lindsten, David Blei
Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models. VI posits a family of…
A general framework for ensemble distribution distillation
Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten +1
Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation. Additionally, ensembles allow…
Deep Gaussian Markov Random Fields
Per Sidén, Fredrik Lindsten
Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures. We esta…
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…
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (…