7 citations · 17 across the 9 of their papers we have counts for
9 papers
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
Chunnan Wang, Xingyu Chen, Chengyue Wu +1
Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design suitable forecasting model for the given time series data scenarios, is a novel…
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy
Chunnan Wang, Hongzhi Wang, Xiangyu Shi
Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource…
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model
Chunnan Wang, Chen Liang, Xiang Chen +1
Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem a…
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…
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
Chunnan Wang, Hongzhi Wang, Guosheng Feng +1
The search space of neural architecture search (NAS) for convolutional neural network (CNN) is huge. To reduce searching cost, most NAS algorithms use fixed outer network level str…
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…
Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem
Chunnan Wang, Hongzhi Wang, Tianyu Mu +2
In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter s…