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Found 7 papers, 2 papers with code
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Interpretable and Steerable Sequence Learning via Prototypes
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning
GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints.
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis
Classification models learn to generalize the associations between data samples and their target classes.
Effectively Fine-tune to Improve Large Multimodal Models for Radiology Report Generation
Writing radiology reports from medical images requires a high level of domain expertise.
