research
∙
07/27/2023
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable prepro...
share
research
∙
02/08/2023
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Irregularly-sampled time series (ITS) are native to high-impact domains ...
research
∙
12/06/2022
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Brain extraction and registration are important preprocessing steps in n...
research
∙
12/06/2022
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Deformable image registration, i.e., the task of aligning multiple image...
research
∙
08/21/2022
Stop Hop: Early Classification of Irregular Time Series
Early classification algorithms help users react faster to their machine...
research
∙
01/23/2022
One-Shot Learning on Attributed Sequences
One-shot learning has become an important research topic in the last dec...
research
∙
09/15/2021
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as d...
research
∙
01/13/2021
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Sparse inverse covariance estimation (i.e., edge de-tection) is an impor...
research
∙
11/08/2020
MLAS: Metric Learning on Attributed Sequences
Distance metric learning has attracted much attention in recent years, w...
research
∙
11/03/2019
Attributed Sequence Embedding
Mining tasks over sequential data, such as clickstreams and gene sequenc...
research
∙
05/01/2019
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an ...
research
∙
09/12/2018
Higher-order Graph Convolutional Networks
Following the success of deep convolutional networks in various vision a...
research
∙
08/22/2018
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have shown great capacity on imag...
research
∙
02/07/2018
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods...
research
∙
10/25/2017
Inductive Representation Learning in Large Attributed Graphs
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
research
∙
09/15/2017
Deep Graph Attention Model
Graph classification is a problem with practical applications in many di...
research
∙
09/14/2017
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms ...
research
∙
08/19/2015
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Mining discriminative subgraph patterns from graph data has attracted gr...
research
∙
05/20/2013
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Collective classification has been intensively studied due to its impact...
research
∙
01/28/2013
