Sökning: "Florian T. Pokorny"

Hittade 3 avhandlingar innehållade orden Florian T. Pokorny.

  1. 1. The Bergman Kernel on Toric Kähler Manifolds

    Författare :Florian T. Pokorny; Michael Singer; Toby Bailey; The University of Edinburgh School of Mathematics; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

    Sammanfattning : Let $(L,h)\to (X, \omega)$ be a compact toric polarized Kähler manifold of complex dimension $n$. For each $k\in N$, the fibre-wise Hermitian metric $h^k$ on $L^k$ induces a natural inner product on the vector space $C^{\infty}(X, L^k)$ of smooth global sections of $L^k$ by integration with respect to the volume form $\frac{\omega^n}{n!}$. LÄS MER

  3. 2. Synergies between Policy Learning and Sampling-based Planning

    Författare :Robert Gieselmann; Florian T. Pokorny; Edward Johns; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Robotics; Reinforcement Learning; Motion Planning; Robotic Manipulation; Datalogi; Computer Science;

    Sammanfattning : Recent advances in artificial intelligence and machine learning have significantly impacted the field of robotics and led to the interdisciplinary study of robot learning. These developments have the potential to revolutionize the automation of tasks in various industries by reducing the reliance on human workers. LÄS MER

  4. 3. Breaking the Dimensionality Curse of Voronoi Tessellations

    Författare :Vladislav Polianskii; Florian T. Pokorny; Michael Bronstein; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; geometric methods; machine learning methods; Voronoi; Delaunay; high dimensional geometry; curse of dimensionality; monte carlo; Datalogi; Computer Science;

    Sammanfattning : Considering the broadness of the area of artificial intelligence, interpretations of the underlying methodologies can be commonly narrowed down to either a probabilistic or a geometric point of view. Such separation is especially prevalent in more classical "pre-neural-network" machine learning if one compares Bayesian modelling with more deterministic models like nearest neighbors. LÄS MER