- 学术相关
- Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels
- Mega or Micro? Influencer Selection Using Follower Elasticity
- The evolution of forecasting for decision-making in dynamic environments
- JMLR: 异构多智能体强化学习
- Curvy Digital Marketing Designs: Virtual Elements with Rounded Shapes Enhance Online Click-Through Rates
- Logs with Zeros? Some Problems and Solutions
- Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
- 深度学习常用损失函数总览：基本形式、原理、特点
- 数字世界中的大模型Agent：机遇与风险
- 业界动态
- 技术技巧
学术相关
Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels1
Mega or Micro? Influencer Selection Using Follower Elasticity2
The evolution of forecasting for decision-making in dynamic environments3
JMLR: 异构多智能体强化学习4
Curvy Digital Marketing Designs: Virtual Elements with Rounded Shapes Enhance Online Click-Through Rates5
Logs with Zeros? Some Problems and Solutions6
Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf7
深度学习常用损失函数总览：基本形式、原理、特点
数字世界中的大模型Agent：机遇与风险
业界动态
货拉拉利用时空熵平衡提升营销效率的实践
技术技巧
GIS导航网站
从头开始构建LLM (book)
Chrome 扩展
Google 2023最受欢迎的Chrome扩展程序
- Scribe：使用 AI 记录工作流程，并创建分步指南，轻松培训和指导同事
- QuillBot：快速撰写和回复电子邮件，使用 AI 辅助写作和修订
- Sider：浏览器内的侧边栏，可让用户使用 ChatGPT、Claude 和 Bard 等生成式 AI 工具，而无需打开另一个标签页
- Bonjourr：自定义浏览器外观工具，可以让用户的新标签页保持干净、美观且不受干扰
Python & R丨UpSet图（高维韦恩图）
Django丨后台界面插件Django-jet
作者团队还开发了一个商用低代码（？）的框架，网页上有很多CSS可以抄，挺精致的
庄闪闪丨LaTeX 实现表格内换行
- 宏包：\usepackage{makecell}
1
\makecell[位置：c l r]{第1行内容 \\ 第2行内容 \\ 第3行内容 ...}
用途：如表格内参数下的标准误
前后端技术资源合集
JavaScript Django等
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Dong, B., Zhuang, M., Fang, E. (Er), & Huang, M. (2023). Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels. Journal of Marketing, 00222429231190021. https://doi.org/10.1177/00222429231190021 ↩
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Tian, Z., Dew, R., & Iyengar, R. (2023). Mega or Micro? Influencer Selection Using Follower Elasticity. Journal of Marketing Research, 00222437231210267. https://doi.org/10.1177/00222437231210267 ↩
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Tilman, A. R., Vasconcelos, V. V., Akçay, E., & Plotkin, J. B. (2023). The evolution of forecasting for decision-making in dynamic environments. Collective Intelligence, 2(4), 26339137231221726. https://doi.org/10.1177/26339137231221726 ↩
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Kuba, J. G., Feng, X., Ding, S., Dong, H., Wang, J., & Yang, Y. (2022). Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL (arXiv:2208.01682). arXiv. http://arxiv.org/abs/2208.01682 ↩
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Biswas, D., Abell, A., & Chacko, R. (2023). Curvy Digital Marketing Designs: Virtual Elements with Rounded Shapes Enhance Online Click-Through Rates. Journal of Consumer Research, ucad078. https://doi.org/10.1093/jcr/ucad078 ↩
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Chen, J., & Roth, J. (2023). Logs with Zeros? Some Problems and Solutions. *The Quarterly Journal of Economics, qjad054. https://doi.org/10.1093/qje/qjad054 ↩
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Xu, Y., Wang, S., Li, P., Luo, F., Wang, X., Liu, W., & Liu, Y. (2023). Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf (arXiv:2309.04658). arXiv. http://arxiv.org/abs/2309.04658 ↩