| Message scheduling methods for belief propagation C Knoll, M Rath, S Tschiatschek, F Pernkopf Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 21 | 2015 |
| On Loopy Belief Propagation–Local Stability Analysis for Non-Vanishing Fields C Knoll, F Pernkopf Uncertainty in Artificial Intelligence, 2017 | 19 | 2017 |
| Fixed points of belief propagation—an analysis via polynomial homotopy continuation C Knoll, D Mehta, T Chen, F Pernkopf IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (9), 2124-2136, 2017 | 18 | 2017 |
| Active bayesian causal inference C Toth, L Lorch, C Knoll, A Krause, F Pernkopf, R Peharz, J Von Kügelgen Advances in Neural Information Processing Systems 35, 16261-16275, 2022 | 15 | 2022 |
| Belief propagation: Accurate marginals or accurate partition function–where is the difference? C Knoll, F Pernkopf Uncertainty in Artificial Intelligence, 627-636, 2020 | 9 | 2020 |
| Self-Guided Belief Propagation--A Homotopy Continuation Method C Knoll, A Weller, F Pernkopf arXiv preprint arXiv:1812.01339, 2018 | 9 | 2018 |
| Distribution mismatch correction for improved robustness in deep neural networks A Fuchs, C Knoll, F Pernkopf arXiv preprint arXiv:2110.01955, 2021 | 5 | 2021 |
| Convergence behavior of belief propagation: estimating regions of attraction via lyapunov functions H Leisenberger, C Knoll, R Seeber, F Pernkopf Uncertainty in Artificial Intelligence, 1863-1873, 2021 | 3 | 2021 |
| Fixing the bethe approximation: How structural modifications in a graph improve belief propagation H Leisenberger, F Pernkopf, C Knoll Uncertainty in Artificial Intelligence, 1085-1095, 2022 | 2 | 2022 |
| Understanding the Behavior of Belief Propagation: Convergence Properties, Approximation Quality, and Solution Space Analysis C Knoll | 2* | |
| Self-attention for enhanced OAMP detection in MIMO systems A Fuchs, C Knoll, NN Moghadam, A Pak, J Huang, E Leitinger, F Pernkopf ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 1 | 2023 |
| Fixed point solutions of belief propagation C Knoll, F Pernkopf, D Mehta, T Chen NIPS-Workshop: Advances in Approximate Bayesian Inference, 2016 | 1 | 2016 |
| The Muculants–a Higher-Order Statistical Approach C Knoll, BC Geiger, G Kubin arXiv preprint arXiv:1506.04518, 2015 | 1 | 2015 |
| Reliable Belief Propagation: Recent Theoretical and Practical Advances C Knoll, F Pernkopf 2023 IEEE 33rd International Workshop on Machine Learning for Signal …, 2023 | | 2023 |
| Guided Selection of Accurate Belief Propagation Fixed Points C Knoll, F Kulmer, F Pernkopf Machine Learning and the Physical Sciences, 2019 | | 2019 |
| Multirate Signal Processing C Knoll, J Kulmer, F Pernkopf, M Rauter, C Feldbauer, K Witrisal, E Rank | | 2018 |
| Cepstral Analysis of Random Variables: Muculants C Knoll, BC Geiger, G Kubin arXiv preprint arXiv:1506.04518, 2015 | | 2015 |
| Alternative Descriptions for Random Variables C Knoll, DIB Geiger | | 2014 |
| NNLS and NMF C Knoll, J Mangelberger | | 2012 |
| Discrete Fourier Transform C Knoll, J Kulmer, C Stetco | | |
