BZJZ_Material/文档润色流和知识库构建流/claude-scholar/skills/ml-paper-writing/references/knowledge/structure.md

Paper Structure Patterns

This file contains actionable patterns for organizing ML conference papers, extracted from successful publications.

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Introduction Patterns

Pattern: Contribution Statement Structure

Source: "Attention Is All You Need", NeurIPS (2017) Context: Introducing the main contribution

Pattern:

1. Start with broader context or problem
2. Narrow down to specific limitation
3. Present your approach as solution
4. State clear contribution upfront

Example Template:

[Context/Problem]: Existing approaches struggle with [limitation] due to [reason].

[Our Approach]: We propose [method name], which [key innovation].

[Contribution]: This achieves [result] and enables [capability].

Application: Use this pattern when introducing your main contribution in the first or second paragraph of the introduction.

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Pattern: Bulleted Contribution List

Source: "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019) Context: Summarizing contributions for clarity

Pattern:

• Place near end of Introduction (after Related Work)
• Use 2-4 bullets
• Each bullet: 1-2 lines max (in two-column format)
• Start with strong verbs ("We propose", "We demonstrate", "We show")

Example Template:

Our contributions are three-fold:
- We propose [method], which achieves [result].
- We demonstrate that [technique] improves [metric].
- We show that [approach] enables [new capability].

Application: Use this when you need to clearly delineate multiple contributions for reviewers.

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Pattern: Related Work Organization

Source: "Attention Is All You Need", NeurIPS (2017) Context: Structuring literature review

Pattern:

• Organize methodologically, not chronologically
• Group papers by approach/assumption
• Contrast your approach with each group
• Use "One line of work uses X whereas we use Y because..."

Example Template:

[Approach Category]: Several approaches use [assumption A] [refs].
[Contrast]: We adopt [assumption B] because it allows [benefit].

[Alternative Category]: Other methods focus on [aspect C] [refs].
[Positioning]: We build on this by adding [our innovation].

Application: Use this to position your work relative to existing literature without paper-by-paper reviews.

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Methods Section Patterns

Pattern: Algorithm Presentation

Source: "Adam: A Method for Stochastic Optimization", ICLR (2015) Context: Describing algorithms clearly

Pattern:

1. High-level overview first
2. Mathematical formulation
3. Algorithm pseudocode (if complex)
4. Implementation details

Example Template:

[Overview]: We formulate [problem] as optimization. Let [objective] be our goal.

[Method]: Our approach optimizes [objective] using [technique].
Specifically, we [algorithm description].

[Algorithm]: The full procedure is shown in Algorithm 1.

[Implementation]: In practice, we [practical details].

Application: Use this when presenting novel algorithms or optimization methods.

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Pattern: Component Breakdown

Source: "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019) Context: Describing multi-component systems

Pattern:

• Present model architecture first
• Break down into key components
• Explain each component's role
• Show how components interact

Example Template:

[Architecture]: Our model consists of [N components]: [list].

[Component 1]: The [component] module [function].
[Component 2]: The [component] layer [operation].

[Integration]: These components are stacked sequentially, with [connection pattern].

Application: Use this when describing complex architectures with multiple interacting parts.

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Results Section Patterns

Pattern: Quantitative Opening

Source: "BERT: Pre-training of Deep Bidirectional Transformers", NAACL (2019) Context: Presenting main findings

Pattern:

• Start with strongest quantitative result
• Use exact numbers and metrics
• Include comparison to baselines
• State statistical significance

Example Template:

[Main Result]: Our method achieves [score] on [dataset], improving
over the previous best of [baseline] by [margin] (p<0.001).

[Comparison]: Compared to baselines:
- [Method A]: [score]
- [Method B]: [score]
- Ours: [score]

[Significance]: Results are averaged over N runs; standard deviations shown in parentheses.

Application: Use this to open your Results section with your strongest finding.

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Pattern: Table Integration

Source: "Attention Is All All You Need", NeurIPS (2017) Context: Presenting results in tables

Pattern:

• Bold best results in each column
• Include direction indicators (↑↓)
• Provide table caption that stands alone
• Reference table in text before presenting

Example Template:

Table 1 shows our method's performance. Our model (bold) outperforms
all baselines across datasets.

[Table content]

As shown in Table 1, we achieve state-of-the-art on [datasets].

Application: Use this when presenting comparative results in table format.

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Discussion Section Patterns

Pattern: Limitations First

Source: "Attention Is All You Need", NeurIPS (2017) Context: Acknowledging limitations proactively

Pattern:

• State limitations clearly in first paragraph
• Explain why limitations don't undermine core claims
• Distinguish between limitations and future work

Example Template:

[Limitation Statement]: Our approach has [limitation]. Specifically,
[constraint].

[Mitigation]: Despite this, our core findings about [main contribution] remain
valid because [reason].

[Future Work]: Addressing this limitation is an important direction for
future research.

Application: Use this to acknowledge limitations honestly while maintaining paper strength.

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Pattern: Broader Impact Framing

Source: "Language Models are Few-Shot Learners", GPT-3 Paper (2020) Context: Discussing wider implications

Pattern:

• Start with direct implications
• Expand to related domains
• Consider societal impact (if appropriate)
• End with forward-looking statement

Example Template:

[Direct Impact]: Our findings suggest that [implication for domain].

[Broader Implications]: Beyond [specific domain], this approach could
enable [application in other areas].

[Future Outlook]: As [trend] continues, methods like ours will become
increasingly important for [reason].

Application: Use this when writing the final paragraphs of Discussion or Conclusion.

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Transition Patterns

Pattern: Section Transitions

Source: "Attention Is All You Need", NeurIPS (2017) Context: Moving between sections

Pattern:

• Introduction → Methods: "We now describe our approach."
• Methods → Results: "We evaluate our method on [tasks]."
• Results → Discussion: "These results suggest that [insight]."

Example Template:

[Transition to Methods]: Having established [motivation], we present
our method.

[Transition to Results]: To validate our approach, we conduct experiments
on [datasets].

[Transition to Discussion]: The experimental results reveal several insights
about [phenomenon], which we discuss next.

Application: Use these to create smooth transitions between major sections.

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Notes

• Consistency: Maintain consistent terminology throughout the paper
• Flow: Each section should logically lead to the next
• Clarity: Make structure explicit with signposting
• Audience: Write for tired reviewers - make their job easy

何凯明（Kaiming He）的论文结构模式

来源: 分析了何凯明的 19 篇代表性论文 添加时间: {datetime.now().strftime('%Y-%m-%d')}

摘要结构模式

何凯明在摘要中常用的开场模式：

模式 1: 直接陈述贡献

We introduce [method name], a [key feature] framework for [task].
We show that [method] achieves [result] on [dataset].

模式 2: 问题-解决方案

[Problem] is difficult for [task]. We present [solution]
that addresses this by [key mechanism].

示例 (来自 ResNet):

Deeper neural networks are more difficult to train. We present a
residual learning framework to ease the training of networks that
are substantially deeper than those used previously.

引言结构模式

三段式引言:

1. 问题陈述 (2-3段) - 描述挑战和现有方法
2. 方法概述 (1-2段) - 简洁介绍解决方案
3. 主要贡献 (1段) - 列表形式，每条 1-2 行

贡献列表模式:

- 我们提出了 [方法]，解决了 [问题]
- 我们展示了 [方法] 在 [数据集] 上的 [性能提升]
- 我们证明了 [原理] 是有效的

方法部分结构

何凯明的方法部分通常包含：

1. 符号定义 - 清晰定义所有变量和符号
2. 问题形式化 - 数学公式表达
3. 方法描述 - 逐步算法解释
4. 实现细节 - 网络架构、训练设置

常用句式:

• "Let us consider [变量] as [定义]"
• "Formally, we define [公式]"
• "We hypothesize that [假设]"
• "To the extreme, [极端情况]"

实验部分结构

1. 实验设置 - 数据集、评价指标、实现细节
2. 主要结果 - 核心性能对比
3. 消融实验 - 组件分析
4. 可视化分析 - 图表展示

结果描述模式:

• "Table X shows that [结果]"
• "Fig. Y illustrates that [观察]"
• "Our method achieves [指标] on [任务]"
• "This represents a [X]% improvement over baseline"

相关工作部分组织

何凯明倾向于主题式组织而非时间顺序：

好的组织方式:

• "One line of work uses [方法A] [引用], whereas we use [方法B]"
• "[方法A] [引用] assumes [假设], but we show [反驳]"

避免:

• "X et al. introduced [方法]. Y et al. improved [方法]"