ALT: An Automatic System for Long Tail Scenario Modeling
Ya-Lin Zhang Jun Zhou Yankun Ren Yue Zhang Xinxing Yang Meng Li Qitao Shi Longfei Li
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.