1 code implementation • NeurIPS 2023 • Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu, Litong Feng, Wayne Zhang
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
1 code implementation • NeurIPS 2023 • Jiaxing Xu, Yunhan Yang, David Tse Jung Huang, Sophi Shilpa Gururajapathy, Yiping Ke, Miao Qiao, Alan Wang, Haribalan Kumar, Josh McGeown, Eryn Kwon
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.
1 code implementation • 7 Jul 2021 • Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
1 code implementation • 25 May 2023 • Jiaxing Xu, Aihu Zhang, Qingtian Bian, Vijay Prakash Dwivedi, Yiping Ke
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.
1 code implementation • 9 Sep 2023 • Qingtian Bian, Jiaxing Xu, Hui Fang, Yiping Ke
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.
1 code implementation • 7 Jul 2023 • Jiaxing Xu, Qingtian Bian, Xinhang Li, Aihu Zhang, Yiping Ke, Miao Qiao, Wei zhang, Wei Khang Jeremy Sim, Balázs Gulyás
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
no code implementations • 26 May 2016 • Zhiqiang Xu, Yiping Ke
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.
no code implementations • ICML 2017 • Pengfei Wei, Ramon Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh
A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities.
no code implementations • NeurIPS 2019 • Tianbo Li, Yiping Ke
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.
no code implementations • 6 May 2020 • Pengfei Wei, Yiping Ke, Xinghua Qu, Tze-Yun Leong
Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.
no code implementations • 2 Sep 2022 • Jiaxing Xu, Jinjie Ni, Sophi Shilpa Gururajapathy, Yiping Ke
In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification.