206 citations · 522 across the 32 of their papers we have counts for
8 papers · 1 filter
Improving the Certified Robustness of Neural Networks via Consistency Regularization
Mengting Xu, Tao Zhang, Zhongnian Li +1
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to b…
Transport based Graph Kernels
Kai Ma, Peng Wan, Daoqiang Zhang
Graph kernel is a powerful tool measuring the similarity between graphs. Most of the existing graph kernels focused on node labels or attributes and ignored graph hierarchical stru…
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang +2
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing diff…
Ordinal Pattern Kernel for Brain Connectivity Network Classification
Kai Ma, Biao Jie, Daoqiang Zhang
Brain connectivity networks, which characterize the functional or structural interaction of brain regions, has been widely used for brain disease classification. Kernel-based metho…
Geometric Interpretation of Running Nyström-Based Kernel Machines and Error Analysis
Weida Li, Mingxia Liu, Daoqiang Zhang
Recently, Nyström method has proved its prominence empirically and theoretically in speeding up the training of kernel machines while retaining satisfactory performances and accura…
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Weida Li, Mingxia Liu, Fang Chen +1
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brai…
Gradient Hyperalignment for multi-subject fMRI data alignment
Tonglin Xu, Muhammad Yousefnezhad, Daoqiang Zhang
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make…
Weighted Spectral Cluster Ensemble
Muhammad Yousefnezhad, Daoqiang Zhang
Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increa…