activity
20172022
most citedTensorial Recurrent Neural Networks for Longitudinal Data Analysis

9 citations · 14 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG20223 cited

SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP

Jie Chen, Shouzhen Chen, Mingyuan Bai +3

The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…

cs.LG20211 cited

Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications

Mingyuan Bai, S. T. Boris Choy, Junping Zhang +1

The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., da…

cs.DB20191 cited

Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams

Yang Li, Gang Liu, Junbin Gao +3

Nearest neighbor (NN) problem is an important scientific problem. The NN query, to find the closest one to a given query point among a set of points, is widely used in applications…

cs.LG2019

LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering

Di Xu, Tianhang Long, Junbin Gao

Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing…

cs.LG2019

Tensor-Train Parameterization for Ultra Dimensionality Reduction

Mingyuan Bai, S. T. Boris Choy, Xin Song +1

Locality preserving projections (LPP) are a classical dimensionality reduction method based on data graph information. However, LPP is still responsive to extreme outliers. LPP aim…

cs.LG20179 cited

Tensorial Recurrent Neural Networks for Longitudinal Data Analysis

Mingyuan Bai, Boyan Zhang, Junbin Gao

Traditional Recurrent Neural Networks assume vectorized data as inputs. However many data from modern science and technology come in certain structures such as tensorial time serie…