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Optimizing Loss Functions Through Multivariate Taylor Polynomial
  Parameterization
v1v2v3v4 (latest)

Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization

31 January 2020
Santiago Gonzalez
Risto Miikkulainen
ArXiv (abs)PDFHTML

Papers citing "Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization"

6 / 6 papers shown
Efficient Activation Function Optimization through Surrogate Modeling
Efficient Activation Function Optimization through Surrogate ModelingNeural Information Processing Systems (NeurIPS), 2023
G. Bingham
Risto Miikkulainen
526
9
0
13 Jan 2023
Evolving GAN Formulations for Higher Quality Image Synthesis
Evolving GAN Formulations for Higher Quality Image Synthesis
Santiago Gonzalez
Mohak Kant
Risto Miikkulainen
GAN
246
9
0
17 Feb 2021
Effective Regularization Through Loss-Function Metalearning
Effective Regularization Through Loss-Function MetalearningIEEE Congress on Evolutionary Computation (CEC), 2020
Santiago Gonzalez
Xin Qiu
Risto Miikkulainen
624
5
0
02 Oct 2020
Discovering Parametric Activation Functions
Discovering Parametric Activation Functions
G. Bingham
Risto Miikkulainen
ODL
432
83
0
05 Jun 2020
Meta-learning curiosity algorithms
Meta-learning curiosity algorithmsInternational Conference on Learning Representations (ICLR), 2020
Ferran Alet
Martin Schneider
Tomas Lozano-Perez
L. Kaelbling
272
67
0
11 Mar 2020
Regularized Evolutionary Population-Based Training
Regularized Evolutionary Population-Based TrainingAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2020
J. Liang
Santiago Gonzalez
Hormoz Shahrzad
Risto Miikkulainen
414
11
0
11 Feb 2020
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