Core Insights - Jinqiu Fund is dedicated to supporting innovative founders in the AI sector and engaging with leading AI pioneers to navigate industry transformations [2][3] - The first "AI Pioneers" list was recently announced, highlighting key figures in the AI field who are shaping the future [3][14] Group 1: Evaluation Process - The evaluation panel for the "AI Pioneers" list included industry experts from investment and media, as well as innovative developers and prominent figures from the Zhihu community [4][6] - The selection criteria focused on five dimensions: innovation, critical thinking, influence, ecological value, and development potential [12][13] Group 2: Notable Figures - The list features prominent individuals such as Wang Xingxing, CEO of Yushu Technology, and other leaders from institutions like Carnegie Mellon University and the Chinese Academy of Sciences [3][24][26] - These individuals are recognized for their contributions to AI technology, including advancements in deep learning frameworks and AI applications across various industries [19][21][23] Group 3: Future Outlook - The "AI Pioneers" list serves not only as a recognition of current leaders but also as a record of those exploring new boundaries and developing innovative tools in the AI landscape [28][29] - The ongoing nomination process for the next list encourages broader participation from the community to identify emerging talents in the AI sector [29]

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by three reviewers, and authors must respond to comments, often leading to a significant number of papers being withdrawn from consideration [4][5].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].