Web Reference: 是前n-1步得到的子模型的和。 因此boosting是在sequential地最小化损失函数，其bias自然逐步下降。 但由于是采取这种sequential、adaptive的策略，各子模型之间是强相关的，于是子模型之和并不能显著降低variance。 所以说boosting主要还是靠降低bias来提升预测精度。 （5）Boosting算法对于样本的异常值十分敏感，因为Boosting算法中每个分类器的输入都依赖于前一个分类器的分类结果，会导致误差呈指数级累积。 而用于深度学习模型训练的样本数量很大并且容许一定程度的错误标注，会严重影响模型的性能。 不用额外训练！ALFAR 让多模态大模型吃透检索知识，准确率大幅飙升 | 论文题目：Boosting Knowledge Utilization in Multimodal Large Language Models via Adaptive Logits Fusion and Attention Reallocation 论…
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