A review of semi-supervised learning for text classification
JM Duarte, L Berton - Artificial intelligence review, 2023 - Springer
A huge amount of data is generated daily leading to big data challenges. One of them is
related to text mining, especially text classification. To perform this task we usually need a …
related to text mining, especially text classification. To perform this task we usually need a …
[HTML][HTML] Multimodal data integration for oncology in the era of deep neural networks: a review
Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …
screening and diagnostic imaging to digitized histopathology slides to various types of …
GTR-GA: Harnessing the power of graph-based neural networks and genetic algorithms for text augmentation
A Onan - Expert systems with applications, 2023 - Elsevier
Text augmentation is a popular technique in natural language processing (NLP) that has
been shown to improve the performance of various downstream tasks. The goal of text …
been shown to improve the performance of various downstream tasks. The goal of text …
Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise
X Yu, B Tang, L Deng - Mechanical Systems and Signal Processing, 2023 - Elsevier
Available fault vibration signals of large rotating machines are usually limited and consist of
strong noise. Existing deep learning methods do not sufficiently extract the correlation …
strong noise. Existing deep learning methods do not sufficiently extract the correlation …
H^ 2-MIL: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis
Current representation learning methods for whole slide image (WSI) with pyramidal
resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale …
resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale …
Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Multi-view enhanced graph attention network for session-based music recommendation
Traditional music recommender systems are mainly based on users' interactions, which limit
their performance. Particularly, various kinds of content information, such as metadata and …
their performance. Particularly, various kinds of content information, such as metadata and …
Contrastive graph convolutional networks with adaptive augmentation for text classification
Text classification is an important research topic in natural language processing (NLP), and
Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing …
Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing …
M3GAT: A multi-modal, multi-task interactive graph attention network for conversational sentiment analysis and emotion recognition
Sentiment and emotion, which correspond to long-term and short-lived human feelings, are
closely linked to each other, leading to the fact that sentiment analysis and emotion …
closely linked to each other, leading to the fact that sentiment analysis and emotion …
A survey on semi-supervised graph clustering
Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …