78 citations · 253 across the 56 of their papers we have counts for
6 papers · 1 filter
Bounds all around: training energy-based models with bidirectional bounds
Cong Geng, Jia Wang, Zhiyong Gao +2
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversa…
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain samplin…
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen +1
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the…
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation
Mathias Löwe, Per Lunnemann Hansen, Sebastian Risi
We introduce a novel combination of Bayesian Models (BMs) and Neural Networks (NNs) for making predictions with a minimum expected risk. Our approach combines the best of both worl…
Hierarchical VAEs Know What They Don't Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg +1
Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the…
Sequential Neural Posterior and Likelihood Approximation
Samuel Wiqvist, Jes Frellsen, Umberto Picchini
We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm. SNPLA is a normalizing flows-based algorithm for inference in implicit models, and ther…