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04/25/2023
Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting
Citywide Air Pollution Forecasting tries to precisely predict the air qu...
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04/25/2023
VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data
An end-to-end machine learning (ML) lifecycle consists of many iterative...
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11/03/2022
INGREX: An Interactive Explanation Framework for Graph Neural Networks
Graph Neural Networks (GNNs) are widely used in many modern applications...
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10/20/2022
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation
Graph Neural Networks (GNNs) have become increasingly ubiquitous in nume...
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08/17/2022
Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System
Although the smart camera parking system concept has existed for decades...
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11/29/2020
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars
Paraphrase generation is a long-standing problem and serves an essential...
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11/29/2019
Spatiotemporal deep learning model for citywide air pollution interpolation and prediction
Recently, air pollution is one of the most concerns for big cities. Pred...
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04/21/2018
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM
Tackling air pollution is an imperative problem in South Korea, especial...
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04/21/2018