Company Filing History:
Years Active: 2019-2021
Title: Oleg Borisovich Golovidov: Innovator in Machine Learning
Introduction
Oleg Borisovich Golovidov is a prominent inventor based in Raleigh, NC (US). He has made significant contributions to the field of machine learning, holding a total of 3 patents. His work focuses on enhancing the efficiency and effectiveness of model tuning processes.
Latest Patents
Golovidov's latest patents include a "Hyperparameter Tuning System Results Viewer" and a "Multi-Objective Distributed Hyperparameter Tuning System." The Hyperparameter Tuning System Results Viewer presents a visualization while tuning a machine learning model. This system writes tuning data to a history table, repeatedly training and scoring a model type with different sets of hyperparameter values. An objective function value is computed for each set of hyperparameters, and the data stored in the history table is accessed to identify the hyperparameters. The process involves selecting a page template, updating it with accessed data, and refreshing the display with new data.
The Multi-Objective Distributed Hyperparameter Tuning System determines tuned hyperparameter values for training a machine learning model. It checks if a selected hyperparameter configuration satisfies a linear constraint and utilizes a cache to store previously computed projections. If the projection is not included in the cache, a session is assigned for projection computation, and the results are stored for future use.
Career Highlights
Golovidov is currently employed at SAS Institute Inc., a leading company in analytics and software development. His innovative work in machine learning has positioned him as a key figure in the industry.
Collaborations
He has collaborated with notable coworkers, including Patrick Nathan Koch and Brett Alan Wujek, contributing to various projects that advance machine learning technologies.
Conclusion
Oleg Borisovich Golovidov's contributions to machine learning through his patents and work at SAS Institute Inc. highlight his role as an influential inventor in the field. His innovative approaches to hyperparameter tuning are paving the way for more efficient machine learning models.
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