External Links

Cite Key

Statistics

  • References: 0
  • Cited by: 0
  • Reviews: 0
  • Bibliographies: 0

PDF

Researchr

Researchr is a web site for finding, collecting, sharing, and reviewing scientific publications, for researchers by researchers.

Sign up for an account to create a profile with publication list, tag and review your related work, and share bibliographies with your co-authors.

Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains

Sathya N. Ravi, Abhay Venkatesh, Glenn M. Fung, Vikas Singh. Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. pages 5487-5494, AAAI Press, 2020. [doi]

Authors

Sathya N. Ravi

This author has not been identified. Look up 'Sathya N. Ravi' in Google

Abhay Venkatesh

This author has not been identified. Look up 'Abhay Venkatesh' in Google

Glenn M. Fung

This author has not been identified. Look up 'Glenn M. Fung' in Google

Vikas Singh

This author has not been identified. Look up 'Vikas Singh' in Google
runs on