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English(EN) Learning Non-Vacuous Generalization Bounds from Optimization

新方法为神经网络提供更紧凑的泛化界

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的方法，从优化角度为深度神经网络推导非空泛化界。该方法使用连续时间随机微分方程来模拟离散时间递归过程，这比传统方法提供了更紧凑的界限。研究表明，即使在ImageNet-1K等大型数据集上进行训练，该技术也能为ResNet和Vision Transformer等现代架构提供合理的泛化保证。 AI

影响 提供了一个理论框架，以更好地理解和潜在地提高深度学习模型的泛化能力。

排序理由 该集群包含一篇学术论文，详细介绍了理解神经网络泛化能力的新理论方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法为神经网络提供更紧凑的泛化界

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该集群包含一篇学术论文，详细介绍了理解神经网络泛化能力的新理论方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chengli Tan, Jiangshe Zhang, Junmin Liu, Yihong Gong ·

    从优化中学习非空泛化界限

    arXiv:2206.04359v3 Announce Type: replace Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are ei…

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