24 citations · 110 across the 14 of their papers we have counts for
15 papers
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
Peizhen Bai, Xianyuan Liu, Haiping Lu
Molecular property prediction with deep learning has gained much attention over the past years. Owing to the scarcity of labeled molecules, there has been growing interest in self-…
Multimodal Learning for Multi-Omics: A Survey
Sina Tabakhi, Mohammod Naimul Islam Suvon, Pegah Ahadian +1
With advanced imaging, sequencing, and profiling technologies, multiple omics data become increasingly available and hold promises for many healthcare applications such as cancer d…
Interpretable bilinear attention network with domain adaptation improves drug-target prediction
Peizhen Bai, Filip Miljković, Bino John +1
Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model a…
Uncertainty Estimation for Heatmap-based Landmark Localization
Lawrence Schobs, Andrew J. Swift, Haiping Lu
Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions…
Evaluating Transformers for Lightweight Action Recognition
Raivo Koot, Markus Hennerbichler, Haiping Lu
In video action recognition, transformers consistently reach state-of-the-art accuracy. However, many models are too heavyweight for the average researcher with limited hardware re…
Channel-Temporal Attention for First-Person Video Domain Adaptation
Xianyuan Liu, Shuo Zhou, Tao Lei +1
Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recogniti…
Unifying Homophily and Heterophily Network Transformation via Motifs
Yan Ge, Jun Ma, Li Zhang +1
Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network…
Hop-Hop Relation-aware Graph Neural Networks
Li Zhang, Yan Ge, Haiping Lu
Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper,…
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Hao Xu, Shengqi Sang, Peizhen Bai +2
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing…
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Hao Xu, Shengqi Sang, Haiping Lu
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and protei…
Domain Independent SVM for Transfer Learning in Brain Decoding
Shuo Zhou, Wenwen Li, Christopher R. Cox +1
Brain imaging data are important in brain sciences yet expensive to obtain, with big volume (i.e., large p) but small sample size (i.e., small n). To tackle this problem, transfer…
Mixed-Order Spectral Clustering for Networks
Yan Ge, Haiping Lu, Pan Peng
Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g.,…
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Wenwen Li, Jian Lou, Shuo Zhou +1
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional st…
Graph Node-Feature Convolution for Representation Learning
Li Zhang, Heda Song, Nikolaos Aletras +1
Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature vectors of all neighbors in the aggregation…
Semi-Orthogonal Multilinear PCA with Relaxed Start
Qiquan Shi, Haiping Lu
Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with mult…