AIMC Topic: Semantics

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MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction.

PloS one Nov 15, 2024
Considering that the traditional deep learning event extraction method ignores the correlation between word features and sequence information, it cannot fully explore the hidden associations between events and events and between events and primary at...
Natural Language Processing Semantics Algorithms Deep Learning Humans Neural Networks, Computer
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Semantic-guided attention and adaptive gating for document-level relation extraction.

Scientific reports Nov 4, 2024
In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based model...
Humans Algorithms Natural Language Processing Data Mining Models, Theoretical Attention Semantics
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GradToken: Decoupling tokens with class-aware gradient for visual explanation of Transformer network.

Neural networks : the official journal of the International Neural Network Society Nov 1, 2024
Transformer networks have been widely used in the fields of computer vision, natural language processing, graph-structured data analysis, etc. Subsequently, explanations of Transformer play a key role in helping humans understand and analyze its deci...
Attention Semantics Neural Networks, Computer Algorithms Humans
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SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

European radiology Oct 29, 2024
OBJECTIVES: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo ...
Aged Male Middle Aged Humans Female Spine Image Interpretation, Computer-Assisted Semantics Deep Learning Magnetic Resonance Imaging
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Synchronization-Inspired Interpretable Neural Networks.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. In...
Cortical Synchronization Brain Models, Neurological Semantics Nerve Net Neurons Humans Neural Networks, Computer Algorithms
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GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

IEEE transactions on neural networks and learning systems Oct 29, 2024
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...
Tomography, Emission-Computed, Single-Photon Positron-Emission Tomography Semantics Tomography, X-Ray Computed Deep Learning Magnetic Resonance Imaging Multimodal Imaging Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans
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Biologically Plausible Sparse Temporal Word Representations.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...
Humans Neural Networks, Computer Language Models, Neurological Natural Language Processing Algorithms Brain Neurons Semantics
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Exploring better sparsely annotated shadow detection.

Neural networks : the official journal of the International Neural Network Society Oct 22, 2024
Sparsely annotated image segmentation has attracted increasing attention due to its low labeling cost. However, existing weakly-supervised shadow detection methods require complex training procedures, and there is still a significant performance gap ...
Humans Neural Networks, Computer Supervised Machine Learning Semantics Algorithms Image Processing, Computer-Assisted
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Distributional hypothesis as isomorphism between word-word co-occurrence and analogical parallelograms.

PloS one Oct 21, 2024
Most of the modern natural language processing (NLP) techniques are based on the vector space models of language, in which each word is represented by a vector in a high dimensional space. One of the earliest successes was demonstrated by the four-te...
Semantics Models, Theoretical Natural Language Processing Algorithms Language Humans
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Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society Oct 18, 2024
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...
Algorithms Image Processing, Computer-Assisted Semantics Uncertainty Supervised Machine Learning Humans Neural Networks, Computer
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