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English(EN) Deep End-to-End Survival Analysis with Temporal Consistency

新研究探索使用深度学习和优化的先进生存分析技术

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了生存分析的先进技术，这是一种用于预测事件发生时间的预测方法。第一篇论文侧重于结果依赖的截断和审查，证明了使用一阶优化可以有效地学习Cox回归，其性能优于标准的期望最大化算法。第二篇论文引入了一种受强化学习启发的、新颖的深度学习方法，利用延迟目标网络实现稳定的端到端训练，并提高了长序列数据集上的性能。 AI

影响 这些论文推进了生存分析技术，有可能改善医疗保健和金融等领域的预测模型。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了生存分析的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新研究探索使用深度学习和优化的先进生存分析技术

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两篇在arXiv上发表的学术论文，详细介绍了生存分析的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Itai Zilberstein, Ioannis Anagnostides, Arman Kilic, Tuomas Sandholm ·

    Outcome-Dependent Truncation and Censoring下生存分析模型的有效学习

    arXiv:2610.12538v1 Announce Type: new Abstract: Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but practical datasets violate this assumption. We focus on outcome-dependent trunc…

  2. arXiv cs.LG TIER_1 English(EN) · Mariana Vargas Vieyra, Pascal Frossard ·

    深度端到端生存分析与时间一致性

    arXiv:2410.06786v2 Announce Type: replace Abstract: In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learning principles, particularly the Deep Q-Network p…

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