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Found 7 papers, 2 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.
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.
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning.
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.
Real-time Air Pollution prediction model based on Spatiotemporal Big data
In this paper, based on this spatiotemporal Big data, we propose a real-time air pollution prediction model based on Convolutional Neural Network (CNN) algorithm for image-like Spatial distribution of air pollution.
