Abstract
Startups represent newly established business models associated with disruptive innovation and high scalability, hence strongly propel the economic and social development. Meanwhile, startups are heavily constrained by many factors such as limited financial funding and human resources. Therefore, the chance for a startup to succeed is rare like "finding a unicorn in the wild". Venture Capital strives to identify and invest in unicorn startups as early as possible, hoping to gain a high return. This work is traditionally manual and empirical, making it inherently biased and hard to scale. Recently, the rapid growth of data volume and variety is quickly ushering in deep learning (DL) as a potentially superior approach in this domain. In this work, we carry out a literature review and synthesis on DL-based approaches, emphasizing four key aspects: optimization target, feature selection, data split, and evaluation strategy. For each aspect, we summarize our in-depth understanding and practical learning.
