116 citations · 302 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Deep Reasoning Networks: Thinking Fast and Slow
Di Chen, Yiwei Bai, Wenting Zhao +3
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving complex tasks, typically in an unsupervised or weakly-…
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Di Chen, Carla P. Gomes
Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased --- in particular, a…
Understanding Batch Normalization
Johan Bjorck, Carla Gomes, Bart Selman +1
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks. Its tendency to improve accuracy and speed up training have establi…
End-to-End Learning for the Deep Multivariate Probit Model
Di Chen, Yexiang Xue, Carla P. Gomes
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP mod…
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Luming Tang, Yexiang Xue, Di Chen +1
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme…