🤖 machine learning
Drift-Aware Multimodal User Representation Learning via Multi-Scale Temporal Modeling and Sparse Mixture-of-Experts
The paper introduces DUMoE, a unified framework that leverages a temporal dynamics-aware backbone and a sparse mixture-of-experts adapter with a three-stage training strategy to effectively model multi-scale temporal patterns and disentangle diverse, drifting user interests for improved representation learning on social media.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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