AIMC Topic: Neural Networks, Computer

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Predicting chemical ecotoxicity by learning latent space chemical representations.

Environment international
In silico prediction of chemical ecotoxicity (HC) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC yields varia...

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

International journal of computer assisted radiology and surgery
PURPOSE: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep l...

Image Quality Assessment: Unifying Structure and Texture Similarity.

IEEE transactions on pattern analysis and machine intelligence
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original. Relative to human observers, these measures are overly sensitive to resampling of texture regions (e.g., replacing one patch of ...

Convolutional Prototype Network for Open Set Recognition.

IEEE transactions on pattern analysis and machine intelligence
Despite the success of convolutional neural network (CNN) in conventional closed-set recognition (CSR), it still lacks robustness for dealing with unknowns (those out of known classes) in open environment. To improve the robustness of CNN in open-set...

Neural Shape Parsers for Constructive Solid Geometry.

IEEE transactions on pattern analysis and machine intelligence
Constructive solid geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinders. We present CSGNet, a deep network architecture that takes as input...

Optimizing Latent Distributions for Non-Adversarial Generative Networks.

IEEE transactions on pattern analysis and machine intelligence
The generator in generative adversarial networks (GANs) is driven by a discriminator to produce high-quality images through an adversarial game. At the same time, the difficulty of reaching a stable generator has been increased. This paper focuses on...

DiCENet: Dimension-Wise Convolutions for Efficient Networks.

IEEE transactions on pattern analysis and machine intelligence
We introduce a novel and generic convolutional unit, DiCE unit, that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input t...

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN.

Sensors (Basel, Switzerland)
The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp...

Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items.

Sensors (Basel, Switzerland)
In recent years, rotating machinery fault diagnosis methods based on convolutional neural network have achieved much success. However, in real industrial environments, interfering signals are unavoidable, which may reduce the accuracy of fault diagno...

Deep Prior Approach for Room Impulse Response Reconstruction.

Sensors (Basel, Switzerland)
In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural networ...