|
US2021089832A1
|
|
Loss Function Optimization Using Taylor Series Expansion
|
|
US2020346073A1
|
|
Behavior Dominated Search in Evolutionary Search Systems
|
|
WO2020237011A1
|
|
Quantifying the predictive uncertainty of neural networks via residual estimation with i/o kernel
|
|
US2020311556A1
|
|
Process and System Including an Optimization Engine With Evolutionary Surrogate-Assisted Prescriptions
|
|
WO2020186061A1
|
|
System and method for implementing modular universal reparameterization for deep multi-task learning across diverse domains
|
|
US2020143243A1
|
|
Multiobjective Coevolution of Deep Neural Network Architectures
|
|
US2020012648A1
|
|
Systems and Methods For Providing Data-Driven Evolution of Arbitrary Data Structures
|
|
US2019372935A1
|
|
Systems and Methods For Providing Secure Evolution as a Service
|
|
WO2019157257A1
|
|
System and method for pseudo-task augmentation in deep multitask learning
|
|
US2019244686A1
|
|
Enhanced Optimization With Composite Objectives and Novelty-Diversity Selection
|
|
US2019244110A1
|
|
Enhancing Evolutionary Optimization in Uncertain Environments By Allocating Evaluations Via Multi-Armed Bandit Algorithms
|
|
US2018113977A1
|
|
Filtering of genetic material in incremental fitness evolutionary algorithms based on thresholds
|
|
US2018083996A1
|
|
Detecting behavioral anomaly in machine learned rule sets
|
|
US10599471B1
|
|
Project scheduling in a heterogeneous distributed computing environment
|
|
US2017357896A1
|
|
Content embedding using deep metric learning algorithms
|
|
US2017293849A1
|
|
Distributed rule-based probabilistic time-series classifier
|
|
US2017060963A1
|
|
Data mining management server
|
|
US2017323219A1
|
|
Data mining technique with distributed novelty search
|
