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Found 4 papers, 1 papers with code
Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis
Especially, the pre-training plus fine-tuning approach boosts the accuracy scores of the baseline, achieving the new state-of-the-art.
Graph Residual Flow for Molecular Graph Generation
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
BayesGrad: Explaining Predictions of Graph Convolutional Networks
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.
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