12 citations · 22 across the 6 of their papers we have counts for
6 papers
Differentiable Self-Adaptive Learning Rate
Bozhou Chen, Hongzhi Wang, Chenmin Ba
Learning rate adaptation is a popular topic in machine learning. Gradient Descent trains neural nerwork with a fixed learning rate. Learning rate adaptation is proposed to accelera…
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…
EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
Zhaochong An, Bozhou Chen, Houde Quan +2
Knowledge graph completion aims to predict the new links in given entities among the knowledge graph (KG). Most mainstream embedding methods focus on fact triplets contained in the…
ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence
Hongzhi Wang, Bozhou Chen, Yueyang Xu +2
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI). The system is based on the computational model for con…
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…