371 citations · 402 across the 5 of their papers we have counts for
5 papers
Compressive Sensing via Convolutional Factor Analysis
Xin Yuan, Yunchen Pu, Lawrence Carin
We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned {\em in situ} from the compressed measurements. An alte…
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
Andrew Stevens, Yunchen Pu, Yannan Sun +2
A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal-factor an…
Semantic Compositional Networks for Visual Captioning
Zhe Gan, Chuang Gan, Xiaodong He +5
A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is us…
Variational Autoencoder for Deep Learning of Images, Labels and Captions
Yunchen Pu, Zhe Gan, Ricardo Henao +4
A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of t…
Generative Deep Deconvolutional Learning
Yunchen Pu, Xin Yuan, Lawrence Carin
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yieldi…