Search Results for author: Haiping Lu

Found 14 papers, 4 papers with code

Channel-Temporal Attention for First-Person Video Domain Adaptation

no code implementations17 Aug 2021 Xianyuan Liu, Shuo Zhou, Tao Lei, Haiping Lu

Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures.

Action Recognition Unsupervised Domain Adaptation

Hop-Hop Relation-aware Graph Neural Networks

no code implementations21 Dec 2020 Li Zhang, Yan Ge, Haiping Lu

Graph Neural Networks (GNNs) are widely used in graph representation learning.

Knowledge Graph Embedding

Unifying Homophily and Heterophily Network Transformation via Motifs

no code implementations21 Dec 2020 Yan Ge, Jun Ma, Li Zhang, Haiping Lu

Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated.

Network Embedding Node Classification

GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs

1 code implementation29 Oct 2020 Hao Xu, Shengqi Sang, Peizhen Bai, Laurence Yang, Haiping Lu

Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.

Graph Representation Learning Link Prediction +1

Domain Independent SVM for Transfer Learning in Brain Decoding

no code implementations26 Mar 2019 Shuo Zhou, Wenwen Li, Christopher R. Cox, Haiping Lu

We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks.

Brain Decoding Transfer Learning

Mixed-Order Spectral Clustering for Networks

no code implementations25 Dec 2018 Yan Ge, Haiping Lu, Pan Peng

This paper proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both second-order and third-order structures simultaneously, with two MOSC methods developed based on Graph Laplacian (GL) and Random Walks (RW).

Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI

no code implementations4 Dec 2018 Wenwen Li, Jian Lou, Shuo Zhou, Haiping Lu

While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available.

Semi-Orthogonal Multilinear PCA with Relaxed Start

no code implementations30 Apr 2015 Qiquan Shi, Haiping Lu

However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthogonality constraint.

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