SDB balances behavioral diversity and learning stability in VLN self-improvement by expanding decisions into latent hypotheses, performing reliability-aware aggregation, and applying a regularizer, yielding gains such as SPL 33.73 to 35.93 on REVERIE val-unseen.
Model-agnostic meta-learning for fast adaptation of deep networks,
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The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.
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The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation
SDB balances behavioral diversity and learning stability in VLN self-improvement by expanding decisions into latent hypotheses, performing reliability-aware aggregation, and applying a regularizer, yielding gains such as SPL 33.73 to 35.93 on REVERIE val-unseen.
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Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey
The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.