「Jinqiu Spotlight」 追踪锦秋基金与被投企业的每一个光点与动态， 为创业者传递一线行业风向。 作为一支12年的AI方向投资基金，锦秋基金不仅全力支持 敢于定义AI问题的优秀创始人，也积极与 身处技术前沿的那些AI先行者们对话，一起在产业变革的长坡厚雪中共同学习前进。 不久前， 锦秋基金合伙人臧天宇 作为评委参与首期「AI先行者」榜单评选。 锦秋基金被投企业宇树科技创始人兼CEO王兴兴，以及 中科院计算所副所长先进计算研究中心主任 包云岗、 卡内基梅隆大学助理教授陈天奇、 Manus 首席科学家 季逸超、 BosonAI创始人李沐、 DeepSeek创始人兼CEO梁文锋、 Qwen 大模型负责人 林俊旸、月之暗面研究员 苏剑林、 清华大 学电子工程系教授汪玉、晶泰科技联合创始人温书豪、 月之暗面创始人兼CEO杨植麟、开源项目 vLLM核心贡献者游凯超、地平线创始人余凯、 ControINet 开源项目作者张吕敏、Network Dissection技术开创者周博磊 共同上榜。 锦秋基金很荣幸收到知乎邀请，与同行一起， 发现并赋能这些 AI 领域最具潜力的创业者和学者， 让大家看到先行者们前行的路 ...

Core Insights - The article provides a comprehensive guide for newcomers on how to improve the quality and acceptance rate of research papers in the field of deep learning, based on the author's personal experiences and reflections during the submission process [2][3]. Paper Production and Submission Process - The typical process for producing and submitting a deep learning paper involves generating a good idea or experimental results, expanding on them, and writing a structured paper according to the conference's requirements [3][4]. - After submission, if there are no serious issues, the paper enters the review stage, where feedback is provided by three reviewers, and authors must respond to comments, often leading to a significant number of papers being withdrawn from consideration [4][5]. Importance of Writing Quality - Writing a good paper is crucial as it serves as a vehicle for conveying ideas and can significantly impact an author's career; high-quality papers are more likely to be cited and recognized [7][8]. - The quality of a paper can reflect an author's research achievements, with a few outstanding papers often defining a scholar's career [7]. Innovation and Core Ideas - The concept of novelty is central to deep learning papers, where innovation can be measured by the impact of the problem addressed, the effectiveness of the solution, and the novelty of the methods used [10][11]. - Authors should clearly define their core ideas and potential impact when selecting topics and writing papers, ensuring that their contributions are well-articulated [11]. Writing Techniques - Effective writing in deep learning papers often follows a structured approach, where the title and abstract are critical for attracting readers and matching appropriate reviewers [13][14]. - The introduction should clearly present the importance of the problem and the proposed solution, while the experimental section should demonstrate the effectiveness of the approach [15][16]. Common Reviewer Feedback - Common negative feedback from reviewers includes perceived lack of understanding of the field, unclear contributions, and failure to respect prior work [22][24]. - Authors are encouraged to address potential issues before submission by considering common criticisms and ensuring their papers are well-structured and clearly articulated [22][24].