65 citations · 376 across the 32 of their papers we have counts for
8 papers · 1 filter
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
Shuyin Xia, Xiaochuan Dai, Guoyin Wang +2
Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. T…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
Zero-Shot Recognition via Optimal Transport
Wenlin Wang, Hongteng Xu, Guoyin Wang +2
We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxili…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Rui Wang, Guoyin Wang, Ricardo Henao
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source datase…
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li +3
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples. With insights so garnered, adversarial learning…
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu, Shuyang Dai, Zhe Gan +5
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed m…
Non-iterative Label Propagation in Optimal Leading Forest
Ji Xu, Guoyin Wang
Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization…