37 citations · 127 across the 16 of their papers we have counts for
10 papers · 1 filter
STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling
Shufeng Kong, Tao Yu, Yuanyuan Wei +6
Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning. However, accurate JSDM faces two coupled challenges: environmenta…
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…
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
Joshua Fan, Junwen Bai, Zhiyun Li +2
Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield pre…
Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai, Weiran Wang, Carla Gomes
Self-supervised disentangled representation learning is a critical task in sequence modeling. The learnt representations contribute to better model interpretability as well as the…
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…
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
Junwen Bai, Weiran Wang, Yingbo Zhou +1
We propose Deep Autoencoding Predictive Components (DAPC) -- a self-supervised representation learning method for sequence data, based on the intuition that useful representations…
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…
SWALP : Stochastic Weight Averaging in Low-Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko +3
Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages…