Christian Knoll

Research output

Research output per year 2014 2016 2019 2021 2022 2023 2023

• 10 Conference paper
• 3 Article
• 2 Paper
• 1 Poster
• 2 More
  • 1 Diploma Thesis
  • 1 Doctoral Thesis

Research output per year

Research output per year

Search results

• 2023

  Active Bayesian Causal Inference

  Toth, C., Lorch, L., Knoll, C., Krause, A., Pernkopf, F., Peharz, R. & von Kügelgen, J., Apr 2023, Advances in Neural Information Processing Systems : NeurIPS 2022. Vol. 35.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Reasoning 100%
  • Learning Framework 29%
  • Active Learning 29%
  • Bayesian Perspective 29%
  • Gaussian Process 29%

• Reliable Belief Propagation: Recent Theoretical and Practical Advances

  Knoll, C. & Pernkopf, F., 2023, Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023. Comminiello, D. & Scarpiniti, M. (eds.).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Challenging Application 100%

• Self-attention for enhanced OAMP Detection in MIMO Systems

  Fuchs, A., Knoll, C., Leitinger, E. & Pernkopf, F., 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. 5 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • multiple input multiple output system 100%
  • Output System 100%
• 2022

  Fixing the Bethe Approximation: How Structural Modifications in a Graph Improve Belief Propagation

  Leisenberger, H., Pernkopf, F. & Knoll, C., 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022): Uncertainty in Artificial Intelligence, 1-5 August 2022, Eindhoven, The Netherlands. p. 1085–1095 (Proceedings of Machine Learning Research ; vol. 180).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  Open Access

  • Belief Propagation 100%
  • Free Energy 100%
  • Classical Concept 25%
• 2021

  Convergence Behavior of Belief Propagation: Estimating Regions of Attraction via Lyapunov Functions

  Leisenberger, H., Knoll, C., Seeber, R. & Pernkopf, F., 27 Jul 2021, 37th Conference on Uncertainty in Artificial Intelligence. p. 1863-1873 (Proceedings of Machine Learning Research; vol. 161).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

• Wasserstein Distribution Correction for Improved Robustness in Deep Neural Networks

  Fuchs, A., Knoll, C. & Pernkopf, F., 2021, NeurIPS Workshop DistShift.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
• 2019

  Belief Propagation: Accurate Marginals or Accurate Partition Function - Where is the Difference?

  Knoll, C. & Pernkopf, F., 23 Jul 2019, Uncertainty in Artificial Intelligence.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Marginals 100%
  • Beliefs 100%
  • Models 66%
  • Quantity 33%
  • Demonstrates 33%
• 2017

  On Loopy Belief Propagation – Local Stability Analysis for Non-Vanishing Fields

  Knoll, C. & Pernkopf, F., 12 Aug 2017, Uncertainty in Artificial Intelligence.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
• 2016

  Fixed Points Solutions of Belief Propagation

  Knoll, C., Pernkopf, F., Mehta, D. & Chen, T., 2016, Neural Information Processing Systems (NIPS) workshop.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
• 2015

  Message Scheduling Methods for Belief Propagation

  Knoll, C., Rath, M., Tschiatschek, S. & Pernkopf, F., 2015, Machine Learning and Knowledge Discovery in Databases. Springer Verlag, p. 295 310 p. (Lecture Notes in Computer Science; vol. 9285).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review