402 citations · 476 across the 4 of their papers we have counts for
4 papers
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Jakub Rydzewski, Ming Chen, Tushar K. Ghosh +1
Enhanced sampling methods are indispensable in computational physics and chemistry, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space…
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding +4
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most…
Improve black-box sequential anomaly detector relevancy with limited user feedback
Luyang Kong, Lifan Chen, Ming Chen +2
Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their appl…
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…