33 citations · 212 across the 23 of their papers we have counts for
6 papers · 1 filter
DLME: Deep Local-flatness Manifold Embedding
Zelin Zang, Siyuan Li, Di Wu +5
Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, an…
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
Di Wu, Jie Yang, Mohamad Sawan
Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing appr…
Learning to Simulate Unseen Physical Systems with Graph Neural Networks
Ce Yang, Weihao Gao, Di Wu +1
Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the…
GenURL: A General Framework for Unsupervised Representation Learning
Siyuan Li, Zicheng Liu, Zelin Zang +3
Unsupervised representation learning (URL), which learns compact embeddings of high-dimensional data without supervision, has made remarkable progress recently. However, the develo…
CSP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Di Wu, Yi Shi, Ziyu Wang +2
Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between se…
Unsupervised Deep Manifold Attributed Graph Embedding
Zelin Zang, Siyuan Li, Di Wu +3
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing meth…