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1. (WO2017112291) TECHNOLOGIES FOR DISTRIBUTED MACHINE LEARNING
Latest bibliographic data on file with the International Bureau

Pub. No.: WO/2017/112291 International Application No.: PCT/US2016/063570
Publication Date: 29.06.2017 International Filing Date: 23.11.2016
IPC:
G06F 15/18 (2006.01) ,G06F 15/16 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
15
Digital computers in general; Data processing equipment in general
18
in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
15
Digital computers in general; Data processing equipment in general
16
Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
Applicants:
INTEL CORPORATION [US/US]; 2200 Mission College Boulevard Santa Clara, California 95054, US
Inventors:
PAUL, Arnab; US
CHINYA, Gautham; US
Agent:
KELLETT, Glen M.; US
Priority Data:
14/998,31326.12.2015US
Title (EN) TECHNOLOGIES FOR DISTRIBUTED MACHINE LEARNING
(FR) TECHNOLOGIES POUR L'APPRENTISSAGE AUTOMATIQUE DISTRIBUÉ
Abstract:
(EN) Technologies for distributed machine learning include a mobile compute device to identify an input dataset including a plurality of dataset elements for machine learning and select a subset of the dataset elements. The mobile compute device transmits the subset to a cloud server for machine learning and receives, from the cloud server, a set of learned parameters for local data classification in response to transmitting the subset to the cloud server. The learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements.
(FR) Selon l'invention, des technologies pour l'apprentissage automatique distribué comprennent un dispositif de calcul mobile servant à identifier un ensemble de données d'entrée contenant une pluralité d'éléments d'ensemble de données pour l'apprentissage automatique et sélectionner un sous-ensemble des éléments d'ensemble de données. Le dispositif informatique mobile transmet le sous-ensemble à un serveur nuagique pour l'apprentissage automatique et reçoit, du serveur nuagique, un ensemble de paramètres appris pour la classification de données locales en réponse à la transmission du sous-ensemble au serveur nuagique. Les paramètres appris sont basés sur une expansion de caractéristiques extraites par le serveur nuagique à partir du sous-ensemble des éléments de l'ensemble de données.
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Designated States: 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, JP, KE, KG, 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
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Organization (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (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)
Publication Language: English (EN)
Filing Language: English (EN)
Also published as:
CN108475252DE112016006075