AI Medical Compendium

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

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Two algorithms for improving model-based diagnosis using multiple observations and deep learning.

Neural networks : the official journal of the International Neural Network Society
Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often strug...

When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute.

Neural networks : the official journal of the International Neural Network Society
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural netw...

A rule- and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs.

Neural networks : the official journal of the International Neural Network Society
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...

Continual learning with Bayesian compression for shared and private latent representations.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a new continual learning method with Bayesian Compression for Shared and Private Latent Representations (BCSPLR), which learns a compact model structure while preserving the accuracy. In Shared and Private Latent Representations (...

Fast Co-clustering via Anchor-guided Label Spreading.

Neural networks : the official journal of the International Neural Network Society
The attention towards clustering using anchor graph has grown due to its effectiveness and efficiency. As the most representative points in original data, anchors are also regarded as connecting the sample space to the label space. However, when ther...

A novel ANN-based feature subset selection in multi-scale granular ball neighborhood decision tables.

Neural networks : the official journal of the International Neural Network Society
As an effective data preprocessing method, feature subset selection has been widely explored in recent years. However, the feature subset selection for the Wu-Leung model and its extended model involves high time complexity. Therefore, we combine the...

Paying more attention on backgrounds: Background-centric attention for UAV detection.

Neural networks : the official journal of the International Neural Network Society
Under the advancement of artificial intelligence, Unmanned Aerial Vehicles (UAVs) exhibit efficient flexibility in military reconnaissance, traffic monitoring, and crop analysis. However, the UAV detection faces unique challenges due to the UAV's sma...

Tensor neural networks for high-dimensional Fokker-Planck equations.

Neural networks : the official journal of the International Neural Network Society
We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of s...

On spectral bias reduction of multi-scale neural networks for regression problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spec...

Multi-view learning with enhanced multi-weight vector projection support vector machine.

Neural networks : the official journal of the International Neural Network Society
Multi-view learning aims on learning from the data represented by multiple distinct feature sets. Various multi-view support vector machine methods have been successfully applied to classification tasks. However, the existed methods often face the pr...