no code implementations • 24 Apr 2023 • Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor W. Tsang
Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved translations, which is omitted in previous works.
no code implementations • 9 Mar 2021 • Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor Tsang
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
no code implementations • 24 May 2020 • Yaxin Shi, Yuangang Pan, Donna Xu, Ivor W. Tsang
Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis problems.
no code implementations • 4 Jul 2019 • Yaxin Shi, Yuangang Pan, Donna Xu, Ivor Tsang
Although some works have studied probabilistic interpretation for CCA, these models still require the explicit form of the distributions to achieve a tractable solution for the inference.
no code implementations • 2 Jan 2019 • Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen
Multi-output learning aims to simultaneously predict multiple outputs given an input.
no code implementations • 27 Sep 2018 • Yaxin Shi, Donna Xu, Yuangang Pan, Ivor Tsang
Based on this objective, we present an implicit probabilistic formulation for CCA, named Implicit CCA (ICCA), which provides a flexible framework to design CCA extensions with implicit distributions.
no code implementations • 3 May 2018 • Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan
In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.