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HeKKSaGOn AI Symposium

19–21 Sept 2023
Alte Mensa
Europe/Berlin timezone
  • Overview
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  • Call for Abstracts
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    • Hekksagon_symp-orga@gwdg.de

    Contribution List

    2 / 26
    14. Maximum Entropy Force Field Refinement
    Benjamin Eltzner (MPI NAT Göttingen)
    19/09/2023, 14:05
    Statistics
    Plenary

    Features measured from a protein ensemble, like atom distances, are often not recovered on average in molecular dynamics simulations due to imperfections in the simulated force fields. To remedy this problem, various ensemble refinement methods have been developed. We approach the problem from the maximum entropy point of view, in order to determine a least biased force field modification for...

    26. Infomorphic Networks: A Locally Learning Approach to Neural Computation based on Partial Information Decomposition
    Andreas Schneider (Max Planck Institute for Dynamics and Self-Organization)
    20/09/2023, 16:25
    Statistics
    Plenary

    Neural networks rely on coordination among individual neurons to perform complex tasks, but in the brain, they must operate within the constraints of locality for both computation and learning. Our research uses an information-theoretic approach to better understand how locality affects neural networks' structure and operation. We employ Partial Information Decomposition (PID) to quantify...

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