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20142018
most citedVariational Autoencoder for Deep Learning of Images, Labels and Captions

371 citations · 779 across the 19 of their papers we have counts for

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Showing 2016Show all

7 papers · 1 filter

cs.SD2016★ 4 cited

Adaptive DCTNet for Audio Signal Classification

Yin Xian, Yunchen Pu, Zhe Gan +2

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adapti…

stat.ML2016★ 7 cited

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…

cs.CL2016★ 6 cited

Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

Zhe Gan, Chunyuan Li, Changyou Chen +3

Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers fro…

cs.CV2016★ 18 cited

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…

cs.CL2016★ 16 cited

Learning Generic Sentence Representations Using Convolutional Neural Networks

Zhe Gan, Yunchen Pu, Ricardo Henao +3

We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neur…

cs.CV2016★ 5 cited

Adaptive Feature Abstraction for Translating Video to Text

Yunchen Pu, Martin Renqiang Min, Zhe Gan +1

Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent s…