46 citations · 68 across the 8 of their papers we have counts for
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cs.LG2022
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
q-bio.BM2022★ 46 cited
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino +2
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and ef…
cs.LG2022★ 9 cited
Automated Data Augmentations for Graph Classification
Youzhi Luo, Michael McThrow, Wing Yee Au +4
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations tha…