English(EN) Why not just have one less feature before softmax? [D]

机器学习社区辩论 softmax 优化以减少参数

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-29 05:26

r/MachineLearning 子版块上的一场讨论探讨了神经网络中 softmax 函数的一种理论优化。该提议基于 softmax 输出总和为一的数学特性，暗示有 N-1 个自由度，建议将输入到 softmax 的特征数量减少一个。这可能导致更少的参数和更快的模型收敛，尽管其实际效益受到质疑。 AI

影响如果被证明有效，这种理论优化可以在模型训练和推理方面提供微小的效率提升。

排序理由关于常见机器学习组件技术优化的讨论。

在 r/MachineLearning 阅读 →

Softmax

其他

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

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 关于常见机器学习组件技术优化的讨论。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

r/MachineLearning TIER_1 English(EN) · /u/Kinexity · 2026-09-29 05:26

为什么不在softmax之前少一个特征呢？[D]

<div class="md"><p>Softmax has N inputs and N outputs but it's output only has N-1 degrees of freedom because of the condition that the sum of outputs must be equal to one. Based on this we can figure out that actually we can make due with only N-1 inputs by making…

本文如何被排名

报道来源 [1]

为什么不在softmax之前少一个特征呢？[D]

相关实体

相关话题