AIMC Journal:
IEEE transactions on neural networks and learning systems
Showing 351 to 360 of 780 articles
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.
IEEE transactions on neural networks and learning systems
Jul 6, 2022
Causal discovery from observational data is a fundamental problem in science. Though the linear non-Gaussian acyclic model (LiNGAM) has shown promising results in various applications, it still faces the following challenges in the data with multiple...
Parameterized Luenberger-Type H State Estimator for Delayed Static Neural Networks.
IEEE transactions on neural networks and learning systems
Jul 6, 2022
This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the H state estimation performance of the static neural networks with time-varying delay. The nonlinear...
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.
IEEE transactions on neural networks and learning systems
Jul 6, 2022
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow us to inst...
Deep Graph Learning for Anomalous Citation Detection.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Cit...
Memory-Augmented Generative Adversarial Networks for Anomaly Detection.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the distribution of normal data, additional constraints in the modeling process are needed to distinguish...
Neural Schrödinger Equation: Physical Law as Deep Neural Network.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
We show a new family of neural networks based on the Schrödinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schrödinger equation. These physical quantities can be trai...
Joint Stance and Rumor Detection in Hierarchical Heterogeneous Graph.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Recently, large volumes of false or unverified information (e.g., fake news and rumors) appear frequently in emerging social media, which are often discussed on a large scale and widely disseminated, causing bad consequences. Many studies on rumor de...
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
In this article, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the...
Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Anomaly detection is an important data mining task with numerous applications, such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific complicated task with complicated data, the process of building...
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series.
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This article presents a systematic and comprehensive evaluation of unsuperv...
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