Search Results for author:
Found 14 papers, 7 papers with code
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Continual learning on graphs tackles the problem of training a graph neural network (GNN) where graph data arrive in a streaming fashion and the model tends to forget knowledge from previous tasks when updating with new data.
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness
However, as GNNs may inherit historical bias from training data and lead to discriminatory predictions, the bias of local models can be easily propagated to the global model in distributed settings.
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference
We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts.
Multi-Label Clinical Time-Series Generation via Conditional GAN
In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality of uncommon disease generation.
Weakly-Supervised Metric Learning With Cross-Module Communications for the Classification of Anterior Chamber Angle Images
In this paper, we propose a novel end-to-end framework GCNet for automated Glaucoma Classification based on ACA images or other Glaucoma-related medical images.
A Survey on Societal Event Forecasting with Deep Learning
In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources.
Causal Knowledge Guided Societal Event Forecasting
(ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial.
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes.
Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction
Electronic Health Records (EHR) have been heavily used in modern healthcare systems for recording patients' admission information to hospitals.
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
In this paper, we present an Asynchronous Online Federated Learning (ASO-Fed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from clients.
Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection
In this paper, we evaluate several state-of-the-art models with the specific focus of reducing model bias towards these commonly-attacked identity groups.
Federated Multi-task Hierarchical Attention Model for Sensor Analytics
The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors.
