665 citations · 1.9k across the 37 of their papers we have counts for
14 papers · 1 filter
Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions
Ke Sun, Frank Nielsen
This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information. We relat…
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Ke Sun, Bing Yu, Zhouchen Lin +1
Regularization plays a crucial role in machine learning models, especially for deep neural networks. The existing regularization techniques mainly rely on the i.i.d. assumption and…
A Note on Our Submission to Track 4 of iDASH 2019
Marcel Keller, Ke Sun
iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology. In 2019, one track asked for multi-party computation implementatio…
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang, Ke Sun, Tianheng Cheng +9
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-…
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
Ke Sun, Zhanxing Zhu, Zhouchen Lin
The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient w…
Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training
Xianxu Hou, Ke Sun, Linlin Shen +1
We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output…