![]() ![]() We show that the proposed structure provides efficient coverage of the decision space which leads to state-of-the art classification accuracy and fast training times. The proposed model can be trained by minimizing an error function and it allows an effective and intuitive initialization which avoids poor local minima. We use the disjunctive normal form and approximate the boolean conjunction operations with products to construct a novel network architecture. However, this problem arises from the architecture of neural networks. Several initialization schemes and pre-training methods have been proposed to improve the efficiency and performance of training a neural network. One reason is that the backpropagation algorithm, which is used to train artificial neural networks, usually starts from a random weight initialization which complicates the optimization process leading to long training times and increases the risk of stopping in a poor local minima. ![]() However, in many general and non-vision tasks, neural networks are surpassed by methods such as support vector machines and random forests that are also easier to use and faster to train. They form the basis of the highly successful and popular Convolutional Networks which offer the state-of-the-art performance on several computer visions tasks. Dec, 2016.Īrtificial neural networks are powerful pattern classifiers. ∽isjunctive Normal Networks, In Neurocomputing, Vol. The report also presents strategies and directions for CSE research and education for the next decade. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. However, a combination of disruptive developments-including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers-is redefining the scope and reach of the CSE endeavor. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society and the CSE community is at the core of this transformation. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Research and Education in Computational Science and Engineering, Subtitled Report from a workshop sponsored by the Society for Industrial and Applied Mathematics (SIAM) and the European Exascale Software Initiative (EESI-2), Aug, 2016. In addition to presenting a collection of visualization techniques, which individually highlight different aspects of the data, the coordinated view system forms a cohesive environment for exploring the simulations.We also discuss the findings of our study, which are helping to steer further development of the simulation and strengthening our collaboration with the biomedical engineers attempting to understand the phenomenon. µView combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. In this paper we describe the Myocardial Uncertainty Viewer (muView or µView) system for exploring data stemming from the simulation of cardiac ischemia.
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