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Found 9 papers, 4 papers with code
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa.
UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images
Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.
Learning Graph Filters for Spectral GNNs via Newton Interpolation
Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.
Self-Explainable Graph Neural Networks for Link Prediction
Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Graph neural networks (GNNs) have achieved great success in various graph problems.
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor
In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
TODS: An Automated Time Series Outlier Detection System
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.
