Microsoft

Refined graph encoder embedding via self-training and latent community recovery

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within ...
IEEE

Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting

Abstract: Accurate time series forecasting is critical in a variety of fields, including transportation, weather prediction, energy management, infrastructure monitoring, and finance. Forecasting ...
GitHub

GNODEVAE: A Graph-Based ODE-VAE Enhances Clustering of Single-Cell Data

GNODEVAE is an innovative computational framework that integrates Graph Attention Networks (GAT), Neural Ordinary Differential Equations (NODE), and Variational Autoencoders (VAE). It addresses three ...
GitHub

GCP-VQVAE: A Geometry-Complete Language for Protein 3D Structure

Converting protein tertiary structure into discrete tokens via vector-quantized variational autoencoders (VQ-VAEs) creates a language of 3D geometry and provides a natural interface between sequence ...
Frontiers

DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity

Circular RNAs (circRNAs) are a unique class of non-coding RNAs with stable covalently closed structures that play key regulatory roles in gene expression and drug response. However, experimental ...
IEEE

Anomaly Detection in Medical Images Using Encoder-Attention-2Decoders Reconstruction

Abstract: Anomaly detection (AD) in medical applications is a promising field, offering a cost-effective alternative to labor-intensive abnormal data collection and labeling. However, the success of ...