39 citations · 198 across the 18 of their papers we have counts for
24 papers
Multi-objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu +1
Deep generative models (DGMs) have shown promise in image generation. However, most of the existing work learn the model by simply optimizing a divergence between the marginal dist…
Automatic Realistic Music Video Generation from Segments of Youtube Videos
Sarah Gross, Xingxing Wei, Jun Zhu
A Music Video (MV) is a video aiming at visually illustrating or extending the meaning of its background music. This paper proposes a novel method to automatically generate, from a…
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an al…
Boosting Generative Models by Leveraging Cascaded Meta-Models
Fan Bao, Hang Su, Jun Zhu
Deep generative models are effective methods of modeling data. However, it is not easy for a single generative model to faithfully capture the distributions of complex data such as…
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Haosheng Zou, Hang Su, Shihong Song +1
Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process b…
Learning Random Fourier Features by Hybrid Constrained Optimization
Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
The kernel embedding algorithm is an important component for adapting kernel methods to large datasets. Since the algorithm consumes a major computation cost in the testing phase,…