In this paper, we consider the problem of long tail scenario modeling with
budget limitation, i.e., insufficient human resources for model training stage
and limited time and computing resources for model inference stage. This
problem is widely encountered in various applications, yet has received
deficient attention so far. We present an automatic system named ALT to deal
with this problem. Several efforts are taken to improve the algorithms used in
our system, such as employing various automatic machine learning related
techniques, adopting the meta learning philosophy, and proposing an essential
budget-limited neural architecture search method, etc. Moreover, to build the
system, many optimizations are performed from a systematic perspective, and
essential modules are armed, making the system more feasible and efficient. We
perform abundant experiments to validate the effectiveness of our system and
demonstrate the usefulness of the critical modules in our system. Moreover,
online results are provided, which fully verified the efficacy of our system.

本文介绍了一个名为 ALT 的自动化系统，旨在解决长尾场景建模的问题，此问题在各种应用中经常遇到，但一直缺乏足够的关注。系统采用多种自动机器学习相关技术，包括元学习哲学、基于预算限制的神经架构搜索方法等，同时对系统的各模块进行了优化，为其提供更高的可行性和效率。实验结果表明，系统对于预算限制下的长尾场景建模问题非常有效。