AI Medical Compendium

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

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A universal strategy for smoothing deceleration in deep graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have shown great promise in modeling graph-structured data, but the over-smoothing problem restricts their effectiveness in deep layers. Two key weaknesses of existing research on deep GNN models are: (1) ignoring the ben...

An adversarial transformer for anomalous lamb wave pattern detection.

Neural networks : the official journal of the International Neural Network Society
Lamb waves are widely used for defect detection in structural health monitoring, and various methods are developed for Lamb wave data analysis. This paper presents an unsupervised Adversarial Transformer model for anomalous Lamb wave pattern detectio...

Endpoint-aware audio-visual speech enhancement utilizing dynamic weight modulation based on SNR estimation.

Neural networks : the official journal of the International Neural Network Society
Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration w...

Spiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, ...

Sequential recommendation via agent-based irrelevancy skipping.

Neural networks : the official journal of the International Neural Network Society
Sequential Recommendation is based on modelling sequential dependencies in user interactions to produce subsequent recommendation results. However, due to the diversity of users' interests and the uncertainty of their behaviours, not all historical i...

Disentangled Active Learning on Graphs.

Neural networks : the official journal of the International Neural Network Society
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling...

Contrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of...

Quantum mixed-state self-attention network.

Neural networks : the official journal of the International Neural Network Society
Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language...

Dynamic planning in hierarchical active inference.

Neural networks : the official journal of the International Neural Network Society
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, const...

Dual-view global and local category-attentive domain alignment for unsupervised conditional adversarial domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled sourc...