162 citations · 1.4k across the 47 of their papers we have counts for
5 papers · 1 filter
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
Yiheng Zhu, Zhenqiu Ouyang, Ben Liao +5
Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress h…
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Yiheng Zhu, Jialu Wu, Chaowen Hu +4
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical s…
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh +9
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of…
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh +2
Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during wh…
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
Guangyong Chen, Pengfei Chen, Yujun Shi +3
In this work, we propose a novel technique to boost training efficiency of a neural network. Our work is based on an excellent idea that whitening the inputs of neural networks can…