公开(公告)号：US20240394557A1

公开(公告)日：2024-11-28

申请号：US18202472

申请日：2023-05-26

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Mobina Mahdavi ,  Ali Asgari Khoshouyeh ,  Ali Seyfi ,  Zahra Zohrevand ,  Ritesh Ahuja ,  Moein Owhadi Kareshk ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N20/00

Abstract: In an embodiment, a computer combines first original hyperparameters and second original hyperparameters into combined hyperparameters. In each iteration of a binary search that selects hyperparameters, these are selected: a) important hyperparameters from the combined hyperparameters and b) based on an estimated complexity decrease by including only important hyperparameters as compared to the combined hyperparameters, which only one boundary of the binary search to adjust. For the important hyperparameters of a last iteration of the binary search that selects hyperparameters, a pruned value range of a particular hyperparameter is generated based on a first original value range of the particular hyperparameter for the first original hyperparameters and a second original value range of the same particular hyperparameter for the second original hyperparameters. To accelerate hyperparameter optimization (HPO), the particular hyperparameter is tuned only within the pruned value range to discover an optimal value for configuring and training a machine learning model.

公开(公告)号：US20240070471A1

公开(公告)日：2024-02-29

申请号：US17900779

申请日：2022-08-31

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Moein Owhadi Kareshk ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N3/12

CPC classification number: G06N3/126

Abstract: Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.

公开(公告)号：US20250077876A1

公开(公告)日：2025-03-06

申请号：US18239416

申请日：2023-08-29

Applicant: Oracle International Corporation

Inventor： Moein Owhadi Kareshk ,  Giulia Carocari ,  Yasha Pushak ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N3/09

Abstract: Techniques for selecting machine-learned (ML) models using diversity criteria are provided. In one technique, for each ML model of multiple ML models, output data is generated based on input data to the ML model. Multiple pairs of ML models are identified, where each ML model in the multiple pairs is from the multiple ML models. For each pair of ML models in the multiple pairs of ML models: (1) first output data that was previously generated by a first ML model in the pair is identified; (2) second output data that was previously generated by a second ML model in the pair is identified; (3) a diversity value that is based on the first and second output data is generated; and (4) the diversity value is added to a set of diversity values. A subset of the multiple ML models is selected based on the set of diversity values.

公开(公告)号：US20250013909A1

公开(公告)日：2025-01-09

申请号：US18218970

申请日：2023-07-06

Applicant: Oracle International Corporation

Inventor： Moein Owhadi Kareshk ,  Ali Seyfi ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N20/00

Abstract: From many features and many multidimensional points, a computer generates exploratory training configurations. Each point contains a value for each of the features. Each exploratory training configuration identifies a random subset of the features and a random subset of the points. A performance score is generated for each of the exploratory training configurations. A feature weight is generated for each of the features that is based on the performance scores of the exploratory training configurations whose random subset of features contains the feature. A point weight is generated for each of the points that is based on the performance scores of the exploratory training configurations whose random subset of the many points contains the point. A machine learning model is trained using an optimized training corpus that consists of a subset of the many features based on feature weight and a subset of the many points based on point weight.