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Found 8 papers, 1 papers with code
Enhanced Short Text Modeling: Leveraging Large Language Models for Topic Refinement
Crafting effective topic models for brief texts, like tweets and news headlines, is essential for capturing the swift shifts in social dynamics.
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images.
Representation Learning of Knowledge Graph for Wireless Communication Networks
Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
Sepsis is a leading cause of death in the ICU.
Fractional order graph neural network
This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation.
Realized volatility and parametric estimation of Heston SDEs
We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns $R_t$ and the squared volatilities $V_t$.
