AI Medical Compendium

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

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Uncertainty modeling for inductive knowledge graph embedding.

Neural networks : the official journal of the International Neural Network Society
In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the em...

User preference interaction fusion and swap attention graph neural network for recommender system.

Neural networks : the official journal of the International Neural Network Society
Recommender systems are widely used in various applications. Knowledge graphs are increasingly used to improve recommendation performance by extracting valuable information from user-item interactions. However, current methods do not effectively use ...

Multi-level feature fusion networks for smoke recognition in remote sensing imagery.

Neural networks : the official journal of the International Neural Network Society
Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing dete...

Convergence analysis of deep Ritz method with over-parameterization.

Neural networks : the official journal of the International Neural Network Society
The deep Ritz method (DRM) has recently been shown to be a simple and effective method for solving PDEs. However, the numerical analysis of DRM is still incomplete, especially why over-parameterized DRM works remains unknown. This paper presents the ...

A discriminative multi-modal adaptation neural network model for video action recognition.

Neural networks : the official journal of the International Neural Network Society
Research on video-based understanding and learning has attracted widespread interest and has been adopted in various real applications, such as e-healthcare, action recognition, affective computing, to name a few. Amongst them, video-based action rec...

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Neural networks : the official journal of the International Neural Network Society
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine l...

Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

Neural networks : the official journal of the International Neural Network Society
Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods re...

Temporal multi-modal knowledge graph generation for link prediction.

Neural networks : the official journal of the International Neural Network Society
Temporal Multi-Modal Knowledge Graphs (TMMKGs) can be regarded as a synthesis of Temporal Knowledge Graphs (TKGs) and Multi-Modal Knowledge Graphs (MMKGs), combining the characteristics of both. TMMKGs can effectively model dynamic real-world phenome...

GSE: A global-local storage enhanced video object recognition model.

Neural networks : the official journal of the International Neural Network Society
The presence of substantial similarities and redundant information within video data limits the performance of video object recognition models. To address this issue, a Global-Local Storage Enhanced video object recognition model (GSE) is proposed in...

MIFS: An adaptive multipath information fused self-supervised framework for drug discovery.

Neural networks : the official journal of the International Neural Network Society
The production of expressive molecular representations with scarce labeled data is challenging for AI-driven drug discovery. Mainstream studies often follow a pipeline that pre-trains a specific molecular encoder and then fine-tunes it. However, the ...