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Found 11 papers, 5 papers with code
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG.
Visual Analysis of Multi-outcome Causal Graphs
We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs.
Scalable Optimal Margin Distribution Machine
Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts.
Distributed Optimal Margin Distribution Machine
Optimal margin Distribution Machine (ODM), a newly proposed statistical learning framework rooting in the novel margin theory, demonstrates better generalization performance than the traditional large margin based counterparts.
Deep Co-Attention Network for Multi-View Subspace Learning
For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.
Visual Causality Analysis of Event Sequence Data
Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations.
Task-Oriented Optimal Sequencing of Visualization Charts
A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts.
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
CNNComparator: Comparative Analytics of Convolutional Neural Networks
Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations.
