162 citations · 289 across the 17 of their papers we have counts for
18 papers
AI-enhanced Direct SLAM: A Principled Approach to Unsupervised Learning in Bayesian Inference
Alexander Venus, Benjamin Deutschmann, Alexander Fuchs +2
In this paper, we propose an artificial intelligence (AI)-enhanced hybrid simultaneous localization and mapping (SLAM) method that performs Bayesian inference directly on raw radio…
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
Sophie Steger, Christian Knoll, Bernhard Klein +2
Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional fun…
On the Convexity and Reliability of the Bethe Free Energy Approximation
Harald Leisenberger, Christian Knoll, Franz Pernkopf
The Bethe free energy approximation provides an effective way for relaxing NP-hard problems of probabilistic inference. However, its accuracy depends on the model parameters and pa…
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
Christian Toth, Christian Knoll, Franz Pernkopf +1
The traditional two-stage approach to causal inference first identifies a single causal model (or equivalence class of models), which is then used to answer causal queries. However…
Self-attention for Enhanced OAMP Detection in MIMO Systems
Alexander Fuchs, Christian Knoll, Nima N. Moghadam +3
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Sinceclassical algorithms for symbol detection in MIMO setups require large computational r…
Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll +4
Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of i…
Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks
Alexander Fuchs, Christian Knoll, Franz Pernkopf
Deep neural networks rely heavily on normalization methods to improve their performance and learning behavior. Although normalization methods spurred the development of increasingl…
Einstein-Dirac-Maxwell wormholes: ansatz, construction and properties of symmetric solutions
Jose Luis Blázquez-Salcedo, Christian Knoll, E. Radu
We present a discussion of the traversable wormholes in Einstein-Dirac-Maxwell theory recently reported in e-Print: 2010.07317. This includes a detailed description of the ansatz a…
Traversable wormholes in Einstein-Dirac-Maxwell theory
Jose Luis Blázquez-Salcedo, Christian Knoll, Eugen Radu
We construct a specific example of a class of traversable wormholes in Einstein-Dirac-Maxwell theory in four spacetime dimensions, without needing any form of exotic matter. Restri…
Constructing spherically symmetric Einstein-Dirac systems with multiple spinors: Ansatz, wormholes and other analytical solutions
Jose Luis Blázquez-Salcedo, Christian Knoll
In this paper we present a detailed calculation of an Ansatz that allows to obtain spherically symmetric Einstein-Dirac configurations in -dimensions. We show that this is possi…
Boson and Dirac stars in dimensions
Jose Luis Blázquez-Salcedo, Christian Knoll, Eugen Radu
We present a comparative study of spherically symmetric, localized, particle-like solutions for spin and gravitating fields in a -dimensional, asymptotically flat…
Self-Guided Belief Propagation -- A Homotopy Continuation Method
Christian Knoll, Adrian Weller, Franz Pernkopf
Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP)…
Quasinormal modes of Dirac spinors in the background of rotating black holes in four and five dimensions
Jose Luis Blázquez-Salcedo, Christian Knoll
We study the quasinormal modes of massive Dirac spinors in the background of rotating black holes. In particular, we consider the Kerr geometry as well as the five dimensional Myer…
Solutions of the massive Dirac equation in the near-horizon metric of the extremal five dimensional Myers-Perry black hole with equal angular momenta
Jose Luis Blázquez-Salcedo, Christian Knoll
We study massive Dirac fields in the background of the near-horizon limit of the extremal Myers-Perry black hole in five dimensions. We consider the case in which both angular mome…
Slowly damped quasinormal modes of the massive Dirac field in d-dimensional Tangherlini spacetime
Jose Luis Blázquez-Salcedo, Christian Knoll
We consider quasinormal modes of the massive Dirac field in the background of a Schwarzschild- Tangherlini black hole. Different dimensions of the spacetime are considered, from d…
Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation
Christian Knoll, Franz Pernkopf, Dhagash Mehta +1
Belief propagation (BP) is an iterative method to perform approximate inference on arbitrary graphical models. Whether BP converges and if the solution is a unique fixed point depe…
Charged rotating dilaton black holes with Kaluza-Klein asymptotics
Christian Knoll, Petya Nedkova
We construct a class of stationary and axisymmetric solutions to the 5D Einstein-Maxwell-dilaton gravity, which describe configurations of charged rotating black objects with Kaluz…
Cepstral Analysis of Random Variables: Muculants
Christian Knoll, Bernhard C. Geiger, Gernot Kubin
An alternative parametric description for discrete random variables, called muculants, is proposed. In contrast to cumulants, muculants are based on the Fourier series expansion, r…