37 citations · 77 across the 5 of their papers we have counts for
7 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…
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…