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Found 8 papers, 0 papers with code
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.
Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System
Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability.
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.
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars
Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems.
An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios
Also, we applied existing speaker naming models and the attention-based model to real video to prove that our approach shows comparable accuracy to the existing state-of-the-art models and even higher accuracy in some cases.
Spatiotemporal deep learning model for citywide air pollution interpolation and prediction
In this research, we present many spatiotemporal datasets collected over Seoul city in Korea, which is currently much suffered by air pollution problem as well.
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years.
