AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society
Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer complex relationships and obtain interpretable completion results. However, the traditional multi-hop path completion models mainly focus on the stati...

Lie group convolution neural networks with scale-rotation equivariance.

Neural networks : the official journal of the International Neural Network Society
The weight-sharing mechanism of convolutional kernels ensures the translation equivariance of convolutional neural networks (CNNs) but not scale and rotation equivariance. This study proposes a SIM(2) Lie group-CNN, which can simultaneously keep scal...

Event-based adaptive fixed-time optimal control for saturated fault-tolerant nonlinear multiagent systems via reinforcement learning algorithm.

Neural networks : the official journal of the International Neural Network Society
This article investigates the problem of adaptive fixed-time optimal consensus tracking control for nonlinear multiagent systems (MASs) affected by actuator faults and input saturation. To achieve optimal control, reinforcement learning (RL) algorith...

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

Neural networks : the official journal of the International Neural Network Society
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...

TENet: Targetness entanglement incorporating with multi-scale pooling and mutually-guided fusion for RGB-E object tracking.

Neural networks : the official journal of the International Neural Network Society
There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion. However, existing approaches perform event feature...

Static pinning synchronization control of self-triggered coupling dynamical networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, a new static pinning intermittent control based on resource awareness triggering is proposed. A multi-layer control technique is used to synchronize the coupled neural network. First, a hierarchical network structure including pinned a...

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.

Neural networks : the official journal of the International Neural Network Society
The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological cond...

Revisiting the problem of learning long-term dependencies in recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
Recurrent neural networks (RNNs) are an important class of models for learning sequential behavior. However, training RNNs to learn long-term dependencies is a tremendously difficult task, and this difficulty is widely attributed to the vanishing and...

Two-step graph propagation for incomplete multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Incomplete multi-view clustering addresses scenarios where data completeness cannot be guaranteed, diverging from traditional methods that assume fully observed features. Existing approaches often overlook high-order correlations present in multiple ...

D4A: An efficient and effective defense across agnostic adversarial attacks.

Neural networks : the official journal of the International Neural Network Society
Recent studies show that Graph Neural Networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses in graph data. Previous defenses designed heuristic defense strategies for specific attacks or graph ...