Search Results for author:
Found 14 papers, 1 papers with code
Source-Aware Embedding Training on Heterogeneous Information Networks
Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks.
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
We propose a novel heterogeneous graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis.
Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network
This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks.
Asymmetric Graph Representation Learning
We define an incoming embedding and an outgoing embedding for each node to model its sending and receiving features respectively.
PCA Rerandomization
Mahalanobis distance between treatment group and control group covariate means is often adopted as a balance criterion when implementing a rerandomization strategy.
Methodology
Unit Information Prior for Adaptive Information Borrowing from Multiple Historical Datasets
By applying our UIP methods to phase III clinical trials for investigating the efficacy of memantine in Alzheimer's disease, we illustrate its ability of adaptively borrowing information from multiple historical datasets in the real application.
Methodology
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning
Our method, combining GCNs and distance aware pooling, can integrate the information from all slices in the chest CT scans for optimal decision making, which leads to the state-of-the-art accuracy in the COVID-19 diagnosis and prognosis.
Learning distributed sentence vectors with bi-directional 3D convolutions
Similar to the Bi-LSTM, these n-gram detectors learn both forward and backward distributional semantic knowledge from the sentence tensor.
Efficient Unpaired Image Dehazing with Cyclic Perceptual-Depth Supervision
Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost.
Text classification with pixel embedding
The first two dimensions of the convolutional kernel size equal the size of the word image and the last dimension of the kernel size is $n$.
Nonparametric Functional Approximation with Delaunay Triangulation
We propose a differentiable nonparametric algorithm, the Delaunay triangulation learner (DTL), to solve the functional approximation problem on the basis of a $p$-dimensional feature space.
Adaptive Iterative Hessian Sketch via A-Optimal Subsampling
Iterative Hessian sketch (IHS) is an effective sketching method for modeling large-scale data.
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes.
Bayesian Outdoor Defect Detection
We implement the Bayesian detector in the motion blurred drone images, in which the detector successfully identifies the hail damages on the rough surface and substantially enhances the accuracy of the entire defect detection pipeline.
