Search Results for author: Junjie Liang

Found 7 papers, 1 papers with code

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

no code implementations10 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.

Learning-To-Rank Recommendation Systems

LMLFM: Longitudinal Multi-Level Factorization Machine

1 code implementation11 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.

Variable Selection

How Do We Move: Modeling Human Movement with System Dynamics

no code implementations1 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.

Imitation Learning

Longitudinal Deep Kernel Gaussian Process Regression

no code implementations24 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.

Gaussian Processes regression +1

UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data

no code implementations22 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.

Clustering Variational Inference

PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation

no code implementations22 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.

Benchmarking Domain Adaptation +1

NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks

no code implementations2 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.

Graph Learning Link Prediction +2

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