AIMC Topic: Semantics
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An Improved Multitask Learning Model with Matching Network and Its Application in Traditional Chinese Medicine Syndrome Recommendation.
Journal of healthcare engineering
Apr 26, 2022
Multitask learning (MTL) is an open and challenging problem in various real-world applications, such as recommendation systems, natural language processing, and computer vision. The typical way of conducting multitask learning is establishing some gl...
Semantic projection recovers rich human knowledge of multiple object features from word embeddings.
Nature human behaviour
Apr 14, 2022
How is knowledge about word meaning represented in the mental lexicon? Current computational models infer word meanings from lexical co-occurrence patterns. They learn to represent words as vectors in a multidimensional space, wherein words that are ...
Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks.
Computational intelligence and neuroscience
Apr 13, 2022
Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker's personality analysis and reduces the success rate of social engineering attacks. W...
Natural language processing and String Metric-assisted Assessment of Semantic Heterogeneity method for capturing and standardizing unstructured nursing activities in a hospital setting: a retrospective study.
Annali di igiene : medicina preventiva e di comunita
Apr 12, 2022
BACKGROUND: Nurses record data in electronic health records (EHRs) using different terminologies and coding systems. The purpose of this study was to identify unstructured free-text nursing activities recorded by nurses in EHRs with natural language ...
Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Apr 11, 2022
Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on lear...
Industrial equipment detection algorithm under complex working conditions based on ROMS R-CNN.
PloS one
Apr 7, 2022
In the paper, we proposed a deep learning-based industrial equipment detection algorithm ROMS R-CNN (Rotation Occlusion Multi-Scale Region-CNN). It can solve the problem of inaccurate detection of industrial equipment under complex working conditions...
An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks.
IEEE transactions on neural networks and learning systems
Apr 4, 2022
Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural networ...
Word Embedding Distribution Propagation Graph Network for Few-Shot Learning.
Sensors (Basel, Switzerland)
Mar 30, 2022
Few-shot learning (FSL) is of great significance to the field of machine learning. The ability to learn and generalize using a small number of samples is an obvious distinction between artificial intelligence and humans. In the FSL domain, most graph...
Visualization of medical concepts represented using word embeddings: a scoping review.
BMC medical informatics and decision making
Mar 29, 2022
BACKGROUND: Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language p...
Skeleton-Based Spatio-Temporal U-Network for 3D Human Pose Estimation in Video.
Sensors (Basel, Switzerland)
Mar 28, 2022
Despite the great progress in 3D pose estimation from videos, there is still a lack of effective means to extract spatio-temporal features of different granularity from complex dynamic skeleton sequences. To tackle this problem, we propose a novel, s...
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