AIMC Topic: Neural Networks, Computer

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Recurrent networks with soft-thresholding nonlinearities for lightweight coding.

Neural networks : the official journal of the International Neural Network Society
A long-standing and influential hypothesis in neural information processing is that early sensory networks adapt themselves to produce efficient codes of afferent inputs. Here, we show how a nonlinear recurrent network provides an optimal solution fo...

Reduced- and Full-Order Observers for Delayed Genetic Regulatory Networks.

IEEE transactions on cybernetics
This paper is centered upon the state estimation for delayed genetic regulatory networks. Our aim is at estimating the concentrations of mRNAs and proteins by designing reduced-order and full-order state observers based on available network outputs. ...

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

Magnetic resonance in medicine
PURPOSE: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilag...

A Reading Model from the Perspective of Japanese Orthography: Connectionist Approach to the Hypothesis of Granularity and Transparency.

Journal of learning disabilities
This study presents a computer simulation model of reading in Japanese syllabic kana and morphographic kanji. The model was based on the simulation model developed by Harm and Seidenberg for reading in English. The purpose of building the current mod...

F-norm distance metric based robust 2DPCA and face recognition.

Neural networks : the official journal of the International Neural Network Society
Two-dimensional principal component analysis (2DPCA) employs squared F-norm as the distance metric for dimensionality reduction. It is commonly known that squared F-norm is sensitive to the presence of outliers. To address this problem, we use F-norm...

Robustness of learning algorithms using hinge loss with outlier indicators.

Neural networks : the official journal of the International Neural Network Society
We propose a unified formulation of robust learning methods for classification and regression problems. In the learning methods, the hinge loss is used with outlier indicators in order to detect outliers in the observed data. To analyze the robustnes...

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

Journal of manipulative and physiological therapeutics
OBJECTIVES: This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial ...

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

BMC bioinformatics
BACKGROUND: Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands ...

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

Neural networks : the official journal of the International Neural Network Society
There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel con...

Improving deep convolutional neural networks with mixed maxout units.

PloS one
Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently intro...