1.4k citations · 4.2k across the 73 of their papers we have counts for
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Generative Adversarial Positive-Unlabelled Learning
Ming Hou, Brahim Chaib-draa, Chao Li +1
In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting s…
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
Longhao Yuan, Qibin Zhao, Jianting Cao
In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tenso…
Tensorizing Generative Adversarial Nets
Xingwei Cao, Xuyang Zhao, Qibin Zhao
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models. To capture higher-dimensional distributions, the commo…
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
Longhao Yuan, Qibin Zhao, Jianting Cao
In this paper, we aim at the completion problem of high order tensor data with missing entries. The existing tensor factorization and completion methods suffer from the curse of di…
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
A. Cichocki, A-H. Phan, Q. Zhao +4
Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-ord…
Learning Efficient Tensor Representations with Ring Structure Networks
Qibin Zhao, Masashi Sugiyama, Andrzej Cichocki
Tensor train (TT) decomposition is a powerful representation for high-order tensors, which has been successfully applied to various machine learning tasks in recent years. However,…