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Found 5 papers, 0 papers with code
VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data
Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild.
INGREX: An Interactive Explanation Framework for Graph Neural Networks
Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions.
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation
Therefore, we propose a novel GNN explanation framework named SCALE, which is general and fast for explaining predictions.
PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes
To address these challenges, we propose a multi-level GNN explanation framework based on an observation that GNN is a multimodal learning process of multiple components in graph data.
Real-time Mask Detection on Google Edge TPU
After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing masks in order to reduce risk of front-line workers.
