Search Results for author:
Found 16 papers, 7 papers with code
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
This paper proposes a novel process control and monitoring method for the complex structure of high-dimensional image-based overlay errors (modeled in tensor form), which are collected in semiconductor manufacturing processes.
An Incremental Unified Framework for Small Defect Inspection
Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing.
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
A Co-training Approach for Noisy Time Series Learning
Based on this, we create two views for the input time series through two different encoders.
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data.
Correlated Time Series Forecasting
Representation Learning
+1
MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis
This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized.
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness of missing data.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs.
Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile
Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems.
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data.
