说明

  • 最重要的首先的结果，记录实现了什么效果或者得出什么结论。创新性首先也是在结果上，做到之前人没做过的事情，然后才是评价做的好坏。

  • 注意论文的时间，不同的时间段会有热门的问题，或者热门的方法，方法和效果往往是基于这个特定的时间来做的。有时候当时的一些背景内容会默认大家知道而隐含不说。
  • 基于什么方法，分为什么步骤，分为什么模块。
  • 需要用数学公式让看起来复杂一点，哪怕有时候仅仅是用符号表述一边，然后介绍一下相关的复杂理论。
  • 注意理论是讲一个问题形式化，然后分析。不是问题本身，是人为扩展出来的。

补充资料

Structure of a Computer Science Paper (Top Conference Style)

  1. Abstract — A self-contained summary: problem, approach, key results, impact.
  2. Introduction — Hook → problem → why it’s hard → our idea → contributions → road-map.
  3. Method — Problem formulation + core technical contribution: architecture, algorithm, loss, inference. Preliminaries (if any) are briefly covered here.
  4. Experiments — Datasets, setup, baselines, main results, ablation studies, analysis. Implementation details (framework, hyperparameters, hardware) appear as a subsection.
  5. Related Work — Situate against prior work; highlight gap and how we differ. Often placed after Method so readers can better compare.
  6. Discussion & Limitations — Deeper analysis of failure cases, assumptions, scope. Can merge into Conclusion if short.
  7. Conclusion — Recap contributions, summary of findings, future work.
  8. References (unnumbered)

Common Academic Expressions for Computer Science Papers

  1. Abstract
    • Problem / Motivation: The growing prevalence of … has raised the need for effectively addressing …
    • Limitation of prior work: Existing methods for … suffer from several critical limitations.
    • Challenge statement: Despite considerable progress in …, it remains challenging to …
    • Failure of prior work: Previous approaches often fail to handle … under complex scenarios.
  2. Introduction
    • Background / Hook: Recently, … has attracted significant attention in both academia and industry.
    • Problem importance: Addressing … is crucial for …
    • Gap identification: However, existing approaches are limited in that …
    • Proposal: To fill this gap, we propose a … framework.
    • Contribution list: Our main contributions are summarized as follows:
      1. We design … to solve the problem of …
      2. A new … mechanism is introduced to improve …
      3. Extensive experiments demonstrate that our method outperforms existing approaches.
    • Key insight: The key insight of this work is that …
    • Road-map: The rest of this paper is organized as follows.
  3. Related Work
    • Overview of prior work: Numerous studies have investigated the task of …
    • Historical progression: Early attempts focused on …, while modern works tend to …
    • Critiquing drawbacks: A major drawback of the above methods is that …
    • Contrast with ours: In contrast to our work, these approaches ignore …
    • Positioning ours: Several recent works are most relevant to our research.
    • Summary of gap: To the best of our knowledge, no prior work has addressed …
  4. Method
    • Problem formulation: We formally define the problem as follows.
    • Architecture overview: The overall architecture of our model is illustrated in Figure 1.
    • Component description: The system consists of three main components: …, … and …
    • Module role: This module is responsible for …
    • Backbone selection: We adopt … as the backbone network for feature extraction.
    • Objective / Loss: Formally, we define the loss function as follows:
    • Forward computation: Given an input \(x\), the output can be computed by:
    • Efficiency optimization: To further reduce computational overhead, we optimize …
  5. Experiments
    • Setup — Dataset: We evaluate our method on the public … dataset.
    • Setup — Baselines: We compare our method with several state-of-the-art (SOTA) approaches.
    • Setup — Metrics: We use … as the evaluation metric.
    • Main results: As shown in Table 1, our approach achieves the best performance on all benchmarks.
    • Performance claim: It can be observed that our method outperforms baselines by a clear margin.
    • Effectiveness claim: The improvement demonstrates the effectiveness of our module.
    • Ablation study: We conduct ablation studies to validate the necessity of each component.
    • Analysis — Visualization: The convergence curve is plotted in Figure 2 for intuitive comparison.
    • Analysis — Statistical test: Statistical significance tests are applied to confirm the reliability of results.
    • Implementation Details (subsection)
      • Framework: All experiments are conducted using PyTorch/TensorFlow.
      • Hyperparameters: We use the Adam optimizer with a learning rate of …
      • Hardware: We run our experiments on a machine with an NVIDIA RTX GPU and Intel CPU.
      • Fair comparison: For fair comparison, we keep consistent experimental settings.
  6. Discussion & Limitations
    • General limitation: One potential limitation of our work is …
    • Failure case: The performance degrades slightly when dealing with extreme cases.
    • Possible explanation: Possible reasons for this phenomenon can be explained as follows.
    • Scope discussion: Our method assumes …, which may not hold in …
  7. Conclusion
    • Summary of work: In this paper, we presented … for the task of …
    • Summary of results: Experimental results demonstrate that our method is effective and efficient.
    • Limitation recap: We also discussed several limitations of the current approach.
    • Future direction (general): For future research directions, we plan to extend our model to …
    • Future direction (specific): Another promising direction is to explore lightweight deployment on edge devices.
  8. Transition & Linking Phrases
    • Adding information: Furthermore, … / Moreover, … / In addition, …
    • Emphasis: In particular, … / Specifically, … / Notably, …
    • Result / Consequence: Consequently, … / As a result, … / Therefore, …
    • Contrast: On the contrary, … / However, … / Nevertheless, …
    • Exemplification: For instance, … / For example, … / Such as …

参考论文

bert
transformer