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Chapter and Conference Paper
An Investigation of CNN-CARU for Image Captioning
The goal of an image description is to extract essential information and a description of the content of a media feature from an image. This description can be obtained directly from a human-understandable des...
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Article
Multiple classifier for concatenate-designed neural network
This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet, with the purpose of alleviating the pressure on the final c...
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Chapter and Conference Paper
CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP
This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make ...
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Chapter and Conference Paper
Variable-Depth Convolutional Neural Network for Text Classification
This article introduces a recurrent CNN based framework for the classification of arbitrary length text in natural sentence. In our model, we present a complete CNN design with recurrent structure to capture t...
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Chapter and Conference Paper
Fast Grid-Based Fluid Dynamics Simulation with Conservation of Momentum and Kinetic Energy on GPU
Since the computation of fluid animation is often too heavy to run in real-time simulation, we propose a fast grid-based method with parallel acceleration. In order to reduce the cost of computation kee** a ...
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Chapter and Conference Paper
Simulation of Interaction Between Fluid and Deformable Bodies
Based on the Smoothed Particle Hydrodynamics (SPH) and Finite Element Method (FEM) model, we propose a method for real-time simulation of fluid with deformable bodies. The two-way coupling method for the fluid...
