loadpatents
name:-0.014182090759277
name:-0.011015892028809
name:-0.0037460327148438
GOLOVASHKIN; DMITRY Patent Filings

GOLOVASHKIN; DMITRY

Patent Applications and Registrations

Patent applications and USPTO patent grants for GOLOVASHKIN; DMITRY.The latest application filed is for "forming an artificial neural network by generating and forming of tunnels".

Company Profile
3.7.9
  • GOLOVASHKIN; DMITRY - Morrisville NC
  • Golovashkin; Dmitry - Belmont CA
*profile and listings may contain filings by different individuals or companies with the same name. Review application materials to confirm ownership/assignment.
Patent Activity
PatentDate
Forming An Artificial Neural Network By Generating And Forming Of Tunnels
App 20200272904 - GOLOVASHKIN; DMITRY ;   et al.
2020-08-27
Accelerated Tr-l-bfgs Algorithm For Neural Network
App 20200034713 - Golovashkin; Dmitry ;   et al.
2020-01-30
Accelerated TR-L-BFGS algorithm for neural network
Grant 10,467,528 - Golovashkin , et al. No
2019-11-05
Minimizing global error in an artificial neural network
Grant 10,068,170 - Golovashkin , et al. September 4, 2
2018-09-04
Sharing data structures between processes by semi-invasive hybrid approach
Grant 9,990,303 - Sharanhovich , et al. June 5, 2
2018-06-05
Approach for more efficient use of computing resources while calculating cross product or its approximation for logistic regression on big data sets
Grant 9,870,342 - Golovashkin , et al. January 16, 2
2018-01-16
Sharing Data Structures Between Processes By Semi-invasive Hybrid Approach
App 20170344488 - SHARANHOVICH; ULADZISLAU ;   et al.
2017-11-30
Approach For More Efficient Use Of Computing Resources While Calculating Cross Product Or Its Approximation For Logistic Regression On Big Data Sets
App 20170286365 - Golovashkin; Dmitry ;   et al.
2017-10-05
Sharing data structures between processes by semi-invasive hybrid approach
Grant 9,740,626 - Sharanhovich , et al. August 22, 2
2017-08-22
Approach for more efficient use of computing resources while calculating cross product or its approximation for logistic regression on big data sets
Grant 9,715,481 - Golovashkin , et al. July 25, 2
2017-07-25
Accelerated Tr-l-bfgs Algorithm For Neural Network
App 20170046614 - Golovashkin; Dmitry ;   et al.
2017-02-16
Sharing Data Structures Between Processes By Semi-invasive Hybrid Approach
App 20170046270 - Sharanhovich; Uladzislau ;   et al.
2017-02-16
Approach For More Efficient Use Of Computing Resources While Calculating Cross Product Or Its Approximation For Logistic Regression On Big Data Sets
App 20150378962 - Golovashkin; Dmitry ;   et al.
2015-12-31
Quadratic regularization for neural network with skip-layer connections
Grant 9,047,566 - Golovashkin , et al. June 2, 2
2015-06-02
Minimizing Global Error in an Artificial Neural Network
App 20150088795 - Golovashkin; Dmitry ;   et al.
2015-03-26
Novel Quadratic Regularization For Neural Network With Skip-Layer Connections
App 20140279771 - GOLOVASHKIN; DMITRY ;   et al.
2014-09-18

uspto.report is an independent third-party trademark research tool that is not affiliated, endorsed, or sponsored by the United States Patent and Trademark Office (USPTO) or any other governmental organization. The information provided by uspto.report is based on publicly available data at the time of writing and is intended for informational purposes only.

While we strive to provide accurate and up-to-date information, we do not guarantee the accuracy, completeness, reliability, or suitability of the information displayed on this site. The use of this site is at your own risk. Any reliance you place on such information is therefore strictly at your own risk.

All official trademark data, including owner information, should be verified by visiting the official USPTO website at www.uspto.gov. This site is not intended to replace professional legal advice and should not be used as a substitute for consulting with a legal professional who is knowledgeable about trademark law.

© 2024 USPTO.report | Privacy Policy | Resources | RSS Feed of Trademarks | Trademark Filings Twitter Feed