12 citations · 68 across the 49 of their papers we have counts for
8 papers · 1 filter
Search For Deep Graph Neural Networks
Guosheng Feng, Chunnan Wang, Hongzhi Wang
Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem.…
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
Chunnan Wang, Bozhou Chen, Geng Li +1
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to…
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
Chunnan Wang, Kaixin Zhang, Hongzhi Wang +1
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN model…
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
Tianyu Mu, Hongzhi Wang, Chunnan Wang +1
The great amount of datasets generated by various data sources have posed the challenge to machine learning algorithm selection and hyperparameter configuration. For a specific mac…
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
Bozhou Chen, Kaixin Zhang, Longshen Ou +3
Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-paramete…
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning
Chunnan Wang, Hongzhi Wang, Chang Zhou +1
Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize…
A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics
Hongzhi Wang, Yijie Yang, Yang Song
In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, su…
LSTM-based Flow Prediction
Hongzhi Wang, Yang Song, Shihan Tang
In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed. The algorith…