Search Results for author:
Found 11 papers, 6 papers with code
Stochastic Variance Reduced Riemannian Eigensolver
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities.
Thinning for Accelerating the Learning of Point Processes
Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization.
Subdomain Adaptation with Manifolds Discrepancy Alignment
Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
A Class-Aware Representation Refinement Framework for Graph Classification
In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification.
Data-Driven Network Neuroscience: On Data Collection and Benchmark
This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.
Union Subgraph Neural Networks
We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
