37 citations · 127 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
Joint Unsupervised and Supervised Training for Multilingual ASR
Junwen Bai, Bo Li, Yu Zhang +4
Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing met…
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…
Scaling End-to-End Models for Large-Scale Multilingual ASR
Bo Li, Ruoming Pang, Tara N. Sainath +7
Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data. Existing work has shown positive transfe…
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…