Search Results for author:
Found 4 papers, 2 papers with code
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views.
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
Meta-optimized Contrastive Learning for Sequential Recommendation
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
