2 papers
cs.IR2025
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning
Yuhan Liu, Lin Ning, Neo Wu +5
User sequence modeling is crucial for modern large-scale recommendation systems, as it enables the extraction of informative representations of users and items from their historica…
cs.CV2024
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer
Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad +3
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with…