6 citations · 15 across the 6 of their papers we have counts for
6 papers
Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
Valentin Leonhard Buchner, Lele Cao, Jan-Christoph Kalo +1
Prompt Tuning is emerging as a scalable and cost-effective method to fine-tune Pretrained Language Models (PLMs), which are often referred to as Large Language Models (LLMs). This…
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models
Lele Cao, Vilhelm von Ehrenheim, Astrid Berghult +7
The Private Equity (PE) firms operate investment funds by acquiring and managing companies to achieve a high return upon selling. Many PE funds are thematic, meaning investment pro…
CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
Lele Cao, Vilhelm von Ehrenheim, Mark Granroth-Wilding +4
In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis…
Using Deep Learning to Find the Next Unicorn: A Practical Synthesis
Lele Cao, Vilhelm von Ehrenheim, Sebastian Krakowski +2
Startups often represent newly established business models associated with disruptive innovation and high scalability. They are commonly regarded as powerful engines for economic a…
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data
Lele Cao, Sonja Horn, Vilhelm von Ehrenheim +2
Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth…
PAUSE: Positive and Annealed Unlabeled Sentence Embedding
Lele Cao, Emil Larsson, Vilhelm von Ehrenheim +3
Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural l…