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B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
Neural Information Processing Systems
Assumption 1.1 ensures that the gradient estimator
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OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
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In such cases, re-training the model from scratch could be time-consuming.
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Thrust: Adaptively Propels Large Language Models with External Knowledge
Neural Information Processing Systems
To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary.
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