이 애플리케이션의 일부 콘텐츠는 현재 사용할 수 없습니다.
이 상황이 계속되면 다음 주소로 문의하십시오피드백 및 연락
1. (WO2019028468) COMPUTER SYSTEM FOR BUILDING, TRAINING AND PRODUCTIONIZING MACHINE LEARNING MODELS
국제사무국에 기록된 최신 서지정보    정보 제출

공개번호: WO/2019/028468 국제출원번호: PCT/US2018/045414
공개일: 07.02.2019 국제출원일: 06.08.2018
IPC:
G06N 3/08 (2006.01) ,G06N 5/02 (2006.01) ,G06F 15/18 (2006.01)
G SECTION G — 물리학
06
산술논리연산; 계산; 계수
N
특정 계산모델 방식의 컴퓨터시스템
3
생체모델기반 컴퓨터시스템
02
신경망 모델을 사용하는 것
08
학습방법
G SECTION G — 물리학
06
산술논리연산; 계산; 계수
N
특정 계산모델 방식의 컴퓨터시스템
5
지식기반모델을 이용한 컴퓨터시스템
02
지식표현에 대한 것
G SECTION G — 물리학
06
산술논리연산; 계산; 계수
F
전기에 의한 디지털 데이터처리
15
디지털 컴퓨터 일반; 데이터 처리 장비 일반
18
1회의 동작기간에 컴퓨터 자체에 의해 얻어진 경험에 따라 프로그램이 변화되는 것; 학습기계
출원인:
FAIR IP, LLC [US/US]; 1540 2nd Street, Suite 200 Santa Monica, California 90401, US
발명자:
NGUYEN, David Luan; US
BOREN, David Scott; US
BARNWAL, Abhishek; US
ALI, Babar; US
대리인:
ADAIR, John L.; US
우선권 정보:
62/541,46604.08.2017US
발명의 명칭: (EN) COMPUTER SYSTEM FOR BUILDING, TRAINING AND PRODUCTIONIZING MACHINE LEARNING MODELS
(FR) SYSTÈME INFORMATIQUE POUR CONSTRUIRE, FORMER ET METTRE EN PRODUCTION DES MODÈLES D'APPRENTISSAGE AUTOMATIQUE
요약서:
(EN) A system for building, training and productionizing machine learning models is disclosed. A model training specification is received, and a plurality of sets of hyper-parameters is obtained. Sets of training data and hyper parameter sets are distributed to distributed training systems. Models are trained in parallel using different sets of training data. Models are trained using multiple sets of hyper parameters. A candidate hyper-parameter set is selected, based on a measure of estimated effectiveness of the trained predictive models, and a production predictive model is generated by training a predictive model using the selected candidate hyper-parameter set and the complete set of training data.
(FR) L'invention concerne un système pour construire, former et mettre en production des modèles d'apprentissage automatique. Une spécification de formation de modèle est reçue, et une pluralité d'ensembles d'hyper-paramètres est obtenue. Des ensembles de données de formation et d'ensembles d'hyper-paramètres sont distribués à des systèmes de formation distribués. Des modèles sont formés en parallèle en utilisant des ensembles de données de formation différents. Des modèles sont formés en utilisant de multiples ensembles d'hyper-paramètres. Un ensemble d'hyper-paramètres candidat est sélectionné, sur la base d'une mesure de l'efficacité estimée des modèles prédictifs formés, et un modèle prédictif de production est généré par formation d'un modèle prédictif en utilisant l'ensemble d'hyper-paramètres candidat sélectionné et l'ensemble complet de données de formation.
front page image
지정국: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
아프리카지역 지식재산권기구(ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
유라시아 특허청 (AM, AZ, BY, KG, KZ, RU, TJ, TM)
유럽 특허청(EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
공개언어: 영어 (EN)
출원언어: 영어 (EN)