Lior Tabori

Title: Lior Tabori: Innovator in Machine Learning and Data Matching

Introduction

Lior Tabori is a prominent inventor based in Tel Aviv, Israel. He has made significant contributions to the fields of machine learning and data processing. With a total of 2 patents, Tabori is recognized for his innovative approaches to complex data challenges.

Latest Patents

Tabori's latest patents include a "System and method for one-to-many matching between disparate datasets." This method involves receiving a command to perform a one-to-many matching task between first and second datasets. The datasets are vectorized into embedded datasets, and self-attention models are executed to generate attention datasets that express relationships between features. The method culminates in returning a relationship matrix that encapsulates these relationships.

Another notable patent is "Cross-hierarchical machine learning prediction." This method trains machine learning models using training data from different ontological hierarchical levels. It generates instances of output types by executing trained models on unknown data. The outputs include instances at a different hierarchical level, and a voting classifier is trained to predict selected instances based on specific criteria.

Career Highlights

Lior Tabori is currently employed at Intuit, Inc., where he continues to develop innovative solutions in data processing and machine learning. His work has positioned him as a key figure in the tech industry, particularly in the realm of data analytics.

Collaborations

Tabori collaborates with talented individuals such as Sheer Dangoor and Daniel Ben David. Their combined expertise fosters an environment of innovation and creativity, leading to groundbreaking advancements in their respective fields.

Conclusion

Lior Tabori is a distinguished inventor whose work in machine learning and data matching has made a significant impact. His patents reflect a deep understanding of complex data relationships and innovative problem-solving techniques.