22 citations · 62 across the 11 of their papers we have counts for
11 papers
Rethinking Mobile Block for Efficient Attention-based Models
Jiangning Zhang, Xiangtai Li, Jian Li +7
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) ser…
Spatiotemporal Data Mining: A Survey
Arun Sharma, Zhe Jiang, Shashi Shekhar
Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such a…
A Survey on Spatial and Spatiotemporal Prediction Methods
Zhe Jiang
With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effe…
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
Chengming Zhang, Geng Yuan, Wei Niu +8
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and…
Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors
Zhe Jiang, Marcus Stephen Kirby, Wenchong He +1
In recent years, deep learning techniques (e.g., U-Net, DeepLab) have achieved tremendous success in image segmentation. The performance of these models heavily relies on high-qual…
Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction
Wenchong He, Zhe Jiang
Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mi…
Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint
Arpan Man Sainju, Wenchong He, Zhe Jiang +2
Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are d…
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
Wenchong He, Arpan Man Sainju, Zhe Jiang +1
Given a 3D surface defined by an elevation function on a 2D grid as well as non-spatial features observed at each pixel, the problem of surface segmentation aims to classify pixels…
Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach
Zhe Jiang, Arpan Man Sainju
Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available wit…
Mapping road safety features from streetview imagery: A deep learning approach
Arpan Sainju, Zhe Jiang
Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role i…
Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)
Miao Xie, Zhe Jiang, Arpan Man Sainju
Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existen…