Call for Papers: Data Science and Machine Learning across the Stack (CFP)
• 大类 : 工程技术 - 3区
• 小类 : 计算机：硬件 - 3区
• 小类 : 计算机：软件工程 - 3区
For this special issue of Computer, we seek articles that identify the promise of new techniques, paying careful attention to the realm of the possible and limitations of these techniques. Articles should explain complex technical issues at a level conducive to Computer’s broad readership and how lessons learned from their projects can translate to other domains. The issue aims for a diversity of application areas; example topics include but are not restricted to:
- Data science solutions to classical computer science problems. A range of conferences (for example, SysML) have sprung up to reflect the growing interest in the intersection of machine learning and systems research. Prominent research has revisited classical problems using the new techniques of machine learning (for example, “learned index structures”). What is the potential for ML and data science to upend conventional wisdom and where are the areas where we can see significant improvement?
- New ML programming models and abstractions. As ML permeates more and more domains, programmers need significantly expressive tools to capture problem needs and specify solution strategies. What are the latest approaches to support the new generation of ML programmers and what software abstractions are available?
- Customized hardware solutions to ML problems. GPUs and GPU-based computing usher in a quantum leap in ML capability, and new customized hardware solutions are rapidly being proposed to address the growing demand. What are the latest system architectures to support the next generation of ML applications?
- Human-in-the-loop ML and mining: How can we accelerate human-in-the-loop ML and offer entirely new paradigms of HCI? How can techniques like crowdsourcing coexist with mining massive data?