Sochi, Russia

Aleksei Esin

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Machine Learning Works, LLC. Active years: 2018.

USPTO Granted Patents = 1 

Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2018

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1 patent (USPTO):Explore Patents

Title: Aleksei Esin: Innovator in Neural Network Recognition

Introduction

Aleksei Esin is a prominent inventor based in Sochi, Russia. He has made significant contributions to the field of machine learning, particularly in the area of recognizing mathematical expressions through neural networks. His innovative approach has the potential to enhance various applications in education and scientific research.

Latest Patents

Aleksei holds a patent for a groundbreaking invention titled "Neural network based recognition of mathematical expressions." This patent outlines methods and systems for recognizing characters, including mathematical expressions and chemical formulas. The invention involves a series of steps, including receiving and processing an image, extracting features using a convolutional neural network (CNN), and encoding these features into a distributive representation. The process culminates in decoding the representation into output expressions, which can be presented in a computer-readable format or markup language. Aleksei has 1 patent to his name.

Career Highlights

Aleksei is currently associated with Machine Learning Works, LLC, where he applies his expertise in machine learning and neural networks. His work focuses on developing advanced algorithms that can interpret complex data, making significant strides in the field of artificial intelligence.

Collaborations

Aleksei collaborates with talented individuals in his field, including Pavel Savchenkov and Evgeny Savinov. Their combined efforts contribute to the advancement of machine learning technologies and innovative solutions.

Conclusion

Aleksei Esin's contributions to neural network recognition demonstrate his commitment to innovation in technology. His work not only enhances the understanding of mathematical expressions but also paves the way for future advancements in machine learning.

Profile summary based on public USPTO records.
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