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Found 10 papers, 0 papers with code
OPSD: an Offensive Persian Social media Dataset and its baseline evaluations
While numerous datasets in the English language exist in this domain, few equivalent resources are available for Persian language.
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction.
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
The human brain can be considered as complex networks, composed of various regions that continuously exchange their information with each other, forming the brain network graph, from which nodes and edges are extracted using resting-state functional magnetic resonance imaging (rs-fMRI).
Clustering based on Mixtures of Sparse Gaussian Processes
How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning.
Deep Graph Clustering via Mutual Information Maximization and Mixture Model
In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding.
Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)
Text summarization in medicine can help doctors for reducing the time to access important information from countless documents.
Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine
By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neutral networks.
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference
Probabilistic models, in addition to a better interpretability of the DR methods, provide a framework for further extensions of such algorithms.
An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition
Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction.
Extended Two-Dimensional PCA for Efficient Face Representation and Recognition
r = 1 produces the covariance of 2DPCA, r = n that of PCA.
