94 citations · 226 across the 12 of their papers we have counts for
6 papers · 1 filter
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai, Shufeng Kong, Carla P. Gomes
Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model b…
Materials Representation and Transfer Learning for Multi-Property Prediction
Shufeng Kong, Dan Guevarra, Carla P. Gomes +1
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine…
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Wenting Zhao, Shufeng Kong, Junwen Bai +2
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global chang…
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
Shufeng Kong, Junwen Bai, Jae Hee Lee +6
A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale predicti…
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Junwen Bai, Shufeng Kong, Carla Gomes
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We…