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Found 6 papers, 3 papers with code
Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology.
Self-supervised Learning for Anomaly Detection in Computational Workflows
To address this problem, we introduce an autoencoder-driven self-supervised learning~(SSL) approach that learns a summary statistic from unlabeled workflow data and estimates the normal behavior of the computational workflow in the latent space.
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e. g., graph classification.
Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs
Learning the similarity between structured data, especially the graphs, is one of the essential problems.
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Graph convolution networks (GCNs) have become effective models for graph classification.
TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network
Molecular fingerprints are the workhorse in ligand-based drug discovery.
