# Using Deep Learning to Find the Next Unicorn: A Practical Synthesis

arXiv: https://arxiv.org/abs/2210.14195 · HTML: https://mlsanity.com/paper/2210.14195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 118 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Explaining Explanations: Axiomatic Feature Interactions for Deep Networks](https://mlsanity.com/paper/2002.04138/) (2020)
- [Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method](https://mlsanity.com/paper/2105.03360/) (2021)
- [Graph Neural Network Based VC Investment Success Prediction](https://mlsanity.com/paper/2105.11537/) (2021)
- [Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data](https://mlsanity.com/paper/2208.10375/) (2022)
- [Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods](https://mlsanity.com/paper/2112.07985/) (2021)
- [Where Do You Want To Invest? Predicting Startup Funding From Freely, Publicly Available Web Information](https://mlsanity.com/paper/2204.06479/) (2022)
- [Supervised learning for the prediction of firm dynamics](https://mlsanity.com/paper/2009.06413/) (2020)
- [Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective](https://mlsanity.com/paper/2105.12918/) (2021)

## Built on by (most cited first, 3 of 3)

- [CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification](https://mlsanity.com/paper/2306.10649/) (2023)
- [A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models](https://mlsanity.com/paper/2306.03313/) (2023)
- [Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems](https://mlsanity.com/paper/2309.16888/) (2023)

## Abstract (arXiv)

Startups often represent newly established business models associated with disruptive innovation and high scalability. They are commonly regarded as powerful engines for 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 eventually succeed is as rare as "spotting a unicorn in the wild". Venture Capital (VC) strives to identify and invest in unicorn startups during their early stages, hoping to gain a high return. To avoid entirely relying on human domain expertise and intuition, investors usually employ data-driven approaches to forecast the success probability of startups. Over the past two decades, the industry has gone through a paradigm shift moving from conventional statistical approaches towards becoming machine-learning (ML) based. Notably, the rapid growth of data volume and variety is quickly ushering in deep learning (DL), a subset of ML, as a potentially superior approach in terms of capacity and expressivity. In this work, we carry out a literature review and synthesis on DL-based approaches, covering the entire DL life cycle. The objective is a) to obtain a thorough and in-depth understanding of the methodologies for startup evaluation using DL, and b) to distil valuable and actionable learning for practitioners. To the best of our knowledge, our work is the first of this kind.