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Found 8 papers, 0 papers with code
Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting
Our previous research has solved the problem by considering the whole city as an image and leveraged a Convolutional Long Short-Term Memory (ConvLSTM) model to learn the spatiotemporal features.
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.
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.
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.
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.
