AIMC Topic: Neural Networks, Computer

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Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Journal of computational biology : a journal of computational molecular cell biology
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possi...

Configurable Graph Reasoning for Visual Relationship Detection.

IEEE transactions on neural networks and learning systems
Visual commonsense knowledge has received growing attention in the reasoning of long-tailed visual relationships biased in terms of object and relation labels. Most current methods typically collect and utilize external knowledge for visual relations...

Dual-Path Deep Fusion Network for Face Image Hallucination.

IEEE transactions on neural networks and learning systems
Along with the performance improvement of deep-learning-based face hallucination methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have been used to describe holistic and partial facial features, making the cost o...

Disentangled Representation Learning for Multiple Attributes Preserving Face Deidentification.

IEEE transactions on neural networks and learning systems
Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most...

Simple and Effective: Spatial Rescaling for Person Reidentification.

IEEE transactions on neural networks and learning systems
Global average pooling (GAP) allows convolutional neural networks (CNNs) to localize discriminative information for recognition using only image-level labels. While GAP helps CNNs to attend to the most discriminative features of an object, e.g., head...

Improving Speech Emotion Recognition With Adversarial Data Augmentation Network.

IEEE transactions on neural networks and learning systems
When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation network-namely adversarial data augmentation network...

Dual Position Relationship Transformer for Image Captioning.

Big data
Employing feature vectors extracted from the target detector has been shown to be effective in improving the performance of image captioning. However, it is considered that existing framework suffers from the deficiency of insufficient information ex...

Neural Network Potentials: A Concise Overview of Methods.

Annual review of physical chemistry
In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine...

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Journal of hazardous materials
Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consec...

Exponential synchronization for variable-order fractional discontinuous complex dynamical networks with short memory via impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper considers the exponential synchronization issue for variable-order fractional complex dynamical networks (FCDNs) with short memory and derivative couplings via the impulsive control scheme, where dynamical nodes are modeled to be discontin...