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Computer Graphics

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A clustering-based adaptive Neighborhood Retrieval Visualizer.

Neural networks : the official journal of the International Neural Network Society
We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and ou...

Understanding the message passing in graph neural networks via power iteration clustering.

Neural networks : the official journal of the International Neural Network Society
The mechanism of message passing in graph neural networks (GNNs) is still mysterious. Apart from convolutional neural networks, no theoretical origin for GNNs has been proposed. To our surprise, message passing can be best understood in terms of powe...

Towards real-time photorealistic 3D holography with deep neural networks.

Nature
The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human-computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-a...

TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks.

Neural networks : the official journal of the International Neural Network Society
With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective represen...

Learning temporal attention in dynamic graphs with bilinear interactions.

PloS one
Reasoning about graphs evolving over time is a challenging concept in many domains, such as bioinformatics, physics, and social networks. We consider a common case in which edges can be short term interactions (e.g., messaging) or long term structura...

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization.

IEEE transactions on visualization and computer graphics
Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning...

CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics.

IEEE transactions on visualization and computer graphics
Convolutional neural networks (CNNs) have demonstrated extraordinarily good performance in many computer vision tasks. The increasing size of CNN models, however, prevents them from being widely deployed to devices with limited computational resource...

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...