COMPARATIVE EVALUATION OF THE COMBINED CEREBRAL-CRANIAL TRAUMA IN CHILDREN AND ADULTS

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DIURESIS (FUNCTIONAL) ECHOGRAPHY AND ITS SIGNIFICANCE FOR THE DIAGNOSIS OF CONGENITAL URINARY TRACT ANOMALIES IN CHILDHOOD

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Pareto multi-task deep learning

Neuroevolution has been used to train Deep Neural Networks on reinforcement learning problems. A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for tackling both multi-task and multi-objective problems. In this method prior knowledge on the tasks is used to explicitly define multiple utility functions, which are optimized simultaneously. Experimental results on some Atari 2600 games, a challenging testbed for deep reinforcement learning algorithms, show that a single neural network with a single set of parameters can outperform previous state of the art techniques. In addition to the standard analysis, all results are also evaluated using the Hypervolume indicator and the Kullback-Leibler divergence to get better insights on the underlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks

Юбилейна научна конференция 50 години катедра по педиатрия и медицинска генетика

Варненски медицински форум (Varna Medical Forum) V. 2, Suppl 5(2013