Search Results for author:
Found 31 papers, 8 papers with code
Multi-State Brain Network Discovery
We refer to such brain networks as multi-state, and this mixture can help us understand human behavior.
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies.
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Using this insight, we then propose CAT, a model that classifies multivariate ITS by explicitly seeking highly-relevant portions of an input series' timeline.
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image.
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Our code and data can be found at https://github. com/ERNetERNet/ERNet
Stop&Hop: Early Classification of Irregular Time Series
We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems.
One-Shot Learning on Attributed Sequences
In this paper, we study the problem of one-shot learning on attributed sequences, where each instance is composed of a set of attributes (e. g., user profile) and a sequence of categorical items (e. g., clickstream).
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced.
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities aregenerated from asingleGaussian distribution (i. e., one graph). However, this assumption is too strong for many real-worldapplications.
MLAS: Metric Learning on Attributed Sequences
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback.
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug.
Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?
Motivated by human attention, computational attention mechanisms have been designed to help neural networks adjust their focus on specific parts of the input data.
Attributed Sequence Embedding
This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences.
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons
As a result, even for high-dimensional hidden states, all dimensions are updated at each timestep regardless of the recurrent memory cell.
Adaptive-Halting Policy Network for Early Classification
Early classification of time series is the prediction of the class label of a time series before it is observed in its entirety.
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them.
Higher-order Graph Convolutional Networks
Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator.
Graph Classification using Structural Attention
Graph classification is a problem with practical applications in many different domains.
Ranked #1 on
Graph Classification
on NCI33
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Inductive Representation Learning in Large Attributed Graphs
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
Deep Graph Attention Model
Graph classification is a problem with practical applications in many different domains.
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data.
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views.
A review of heterogeneous data mining for brain disorders
Brain disorder data poses many unique challenges for data mining research.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.
Multilabel Consensus Classification
Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time.
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts.
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type.
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.
