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Neural networks : the official journal of the International Neural Network Society
Nov 22, 2024
Learning on hypergraphs has garnered significant attention recently due to their ability to effectively represent complex higher-order interactions among multiple entities compared to conventional graphs. Nevertheless, the majority of existing method...
Neural networks : the official journal of the International Neural Network Society
Nov 22, 2024
Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as node-wise, layer-wise, and subgraph sampling, have been proposed to allevia...
Neural networks : the official journal of the International Neural Network Society
Nov 22, 2024
Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that...
Neural networks : the official journal of the International Neural Network Society
Nov 22, 2024
This paper studies the finite-time optimal stabilization problem of the macro-micro composite positioning stage (MMCPS). The dynamic model of the MMCPS is established as an interconnected system according to the Newton's second law. Different from ex...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
Quantum-inspired neural networks (QNNs) have shown potential in capturing various non-classical phenomena in language understanding, e.g., the emgerent meaning of concept combinations, and represent a leap beyond conventional models in cognitive scie...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
Learning from functional data with deep neural networks has become increasingly useful, and numerous neural network architectures have been developed to tackle high-dimensional problems raised in practical domains. Despite the impressive practical ac...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
We propose a neural networks method to estimate extreme Expected Shortfall, and even more generally, extreme conditional tail moments as functions of confidence levels, in heavy-tailed settings. The convergence rate of the uniform error between the l...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
Language identification (LID) is a key component in downstream tasks. Recently, the self-supervised speech representation learned by Wav2Vec 2.0 (W2V2) has been demonstrated to be very effective for various speech-related tasks. In LID, it is commonl...
Neural networks : the official journal of the International Neural Network Society
Nov 20, 2024
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion. Based on a message passing mechanism, GNNs have gained increasing popularity due to their intui...