AIMC Topic: Neural Networks, Computer

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Cartographic Relief Shading with Neural Networks.

IEEE transactions on visualization and computer graphics
Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match...

Visual Neural Decomposition to Explain Multivariate Data Sets.

IEEE transactions on visualization and computer graphics
Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a ...

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

IEEE transactions on visualization and computer graphics
To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search...

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data.

IEEE transactions on visualization and computer graphics
The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microst...

A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation.

IEEE transactions on visualization and computer graphics
In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visu...

Dense cellular segmentation for EM using 2D-3D neural network ensembles.

Scientific reports
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has be...

An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.

BioMed research international
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of...

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

PloS one
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...

A proximal neurodynamic model for solving inverse mixed variational inequalities.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity...

Nonclosedness of sets of neural networks in Sobolev spaces.

Neural networks : the official journal of the International Neural Network Society
We examine the closedness of sets of realized neural networks of a fixed architecture in Sobolev spaces. For an exactly m-times differentiable activation function ρ, we construct a sequence of neural networks [Formula: see text] whose realizations co...