Search Results for author:
Found 12 papers, 6 papers with code
DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation
In this paper, we introduce a novel fined-grained knowledge graph structure called an attributed abnormality graph (ATAG).
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data
Metagenomics has equipped us with new avenues of investigating the microbiome, from studying a single species to a complex community in a dynamic ecosystem.
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation
However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images.
Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e. g., correspondence between medications and diagnoses) can often be missing in practice.
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation
In this paper, we propose to introduce a mutual information (MI) term between the input and its latent variable to regularize the objective of the VAE.
Towards well-specified semi-supervised model-based classifiers via structural adaptation
This issue is known as model misspecification.
Aligning Users Across Social Networks Using Network Embedding
Li Liu, 1 William K. Cheung, 2 Xin Li, 1⇤ and Lejian Liao1
Learning Stylometric Representations for Authorship Analysis
Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data.
