Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
Deep learning, graph-based text representation and classification: a survey, perspectives and challenges
Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …
tremendous increase in the amount of complex-structured text resources. These information …
Parameter prediction for unseen deep architectures
Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …
pipelines. However, the algorithms optimizing neural network parameters remain largely …
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy …
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger.
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
Deep graph generators: A survey
Deep generative models have achieved great success in areas such as image, speech, and
natural language processing in the past few years. Thanks to the advances in graph-based …
natural language processing in the past few years. Thanks to the advances in graph-based …
On proximity and structural role-based embeddings in networks: Misconceptions, techniques, and applications
Structural roles define sets of structurally similar nodes that are more similar to nodes inside
the set than outside, whereas communities define sets of nodes with more connections …
the set than outside, whereas communities define sets of nodes with more connections …
H2mn: Graph similarity learning with hierarchical hypergraph matching networks
Graph similarity learning, which measures the similarities between a pair of graph-structured
objects, lies at the core of various machine learning tasks such as graph classification …
objects, lies at the core of various machine learning tasks such as graph classification …
Contrastive brain network learning via hierarchical signed graph pooling model
Recently, brain networks have been widely adopted to study brain dynamics, brain
development, and brain diseases. Graph representation learning techniques on brain …
development, and brain diseases. Graph representation learning techniques on brain …
Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model
Accurate assessment of financial distress of SMEs is critical as it has strong implications for
various stakeholders to understand the firm's financial health. Recent studies start to …
various stakeholders to understand the firm's financial health. Recent studies start to …
Deep learning approaches for similarity computation: A survey
The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …
common but vital task in various domains, such as data mining, machine learning and so on …