10,182 research outputs found
Platonic model of mind as an approximation to neurodynamics
Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view
Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
New machine learning based algorithms have been developed and tested for
Monte Carlo integration based on generative Boosted Decision Trees and Deep
Neural Networks. Both of these algorithms exhibit substantial improvements
compared to existing algorithms for non-factorizable integrands in terms of the
achievable integration precision for a given number of target function
evaluations. Large scale Monte Carlo generation of complex collider physics
processes with improved efficiency can be achieved by implementing these
algorithms into commonly used matrix element Monte Carlo generators once their
robustness is demonstrated and performance validated for the relevant classes
of matrix elements
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