Neural networks : the official journal of the International Neural Network Society
Feb 26, 2025
In feed-forward neural networks, dataset-free weight-initialization methods such as LeCun, Xavier (or Glorot), and He initializations have been developed. These methods randomly determine the initial values of weight parameters based on specific dist...
Neural networks : the official journal of the International Neural Network Society
Feb 25, 2025
In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models...
Neural networks : the official journal of the International Neural Network Society
Feb 25, 2025
Knowledge Tracing (KT), as a pivotal technology in intelligent education systems, analyzes students' learning data to infer their knowledge acquisition and predict their future performance. Recent advancements in KT recognize the importance of memory...
Neural networks : the official journal of the International Neural Network Society
Feb 25, 2025
Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to ext...
Neural networks : the official journal of the International Neural Network Society
Feb 25, 2025
Hyperspectral anomaly detection (HAD) can identify and locate the targets without any known information and is widely applied in Earth observation and military fields. The majority of existing HAD methods use the low-rank representation (LRR) model t...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized and Non-Independently Identically Dist...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, such as approximation by shallow or deep neural networks, radial basis fu...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
This paper presents a specified-time resilient formation maneuver control approach for second-order nonlinear multi-robot systems under false data injection (FDI) attacks, incorporating an offline neural network. Building on existing works in integra...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralize...
Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
Graph Neural Network (GNN) is effective in graph mining and has become a dominant solution to the node classification task. Recently, a series of label reuse approaches emerged to boost the node classification performance of GNN. They repeatedly inpu...