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Found 7 papers, 2 papers with code
Differentiable Self-Adaptive Learning Rate
Learning rate adaptation is a popular topic in machine learning.
Differentiable Hyper-parameter Optimization
We apply our method for the optimization of various neural network layers' hyper-parameters and compare it with multiple benchmark hyper-parameter optimization models.
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures.
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem.
EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
To solve this problem, in this paper, we propose a general framework, named EM-RBR(embedding and rule-based reasoning), capable of combining the advantages of reasoning based on rules and the state-of-the-art models of embedding.
ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI).
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
However, most machine learning algorithms are sensitive to the hyper-parameters.
