no code implementations • 2 Oct 2023 • Xinjie Shen, Danyang Wu, Jitao Lu, Junjie Liang, Jin Xu, Feiping Nie
Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.
no code implementations • 22 Jul 2022 • Yirui Wang, Shenghua He, YouBao Tang, Jingyu Chen, Honghao Zhou, Sanliang Hong, Junjie Liang, Yanxin Huang, Ning Zhang, Ruei-Sung Lin, Mei Han
In order to cope with the increasing demand for labeling data and privacy issues with human detection, synthetic data has been used as a substitute and showing promising results in human detection and tracking tasks.
no code implementations • 22 Oct 2020 • Chacha Chen, Junjie Liang, Fenglong Ma, Lucas M. Glass, Jimeng Sun, Cao Xiao
However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.
no code implementations • 24 May 2020 • Junjie Liang, Yanting Wu, Dongkuan Xu, Vasant Honavar
Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods.
no code implementations • 1 Mar 2020 • Hua Wei, Dongkuan Xu, Junjie Liang, Zhenhui Li
To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.
1 code implementation • 11 Nov 2019 • Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar
However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.
no code implementations • 10 Dec 2018 • Junjie Liang, Jinlong Hu, Shoubin Dong, Vasant Honavar
We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items.