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1. (WO2017132428) METHOD AND SYSTEM FOR DISTRIBUTED DEEP MACHINE LEARNING
Latest bibliographic data on file with the International Bureau

Pub. No.: WO/2017/132428 International Application No.: PCT/US2017/015208
Publication Date: 03.08.2017 International Filing Date: 27.01.2017
IPC:
G06F 17/30 (2006.01) ,G06N 3/063 (2006.01) ,G06N 3/08 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
17
Digital computing or data processing equipment or methods, specially adapted for specific functions
30
Information retrieval; Database structures therefor
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3
Computer systems based on biological models
02
using neural network models
06
Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
063
using electronic means
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3
Computer systems based on biological models
02
using neural network models
08
Learning methods
Applicants:
YAHOO! INC. [US/US]; 701 First Avenue Sunnyvale, California 94089, US
Inventors:
FENG, Andrew; US
SHI, Jun; US
JAIN, Mridul; US
CNUDDE, Peter; US
Agent:
WANG, Tairan; US
BARUFKA, Jack; US
WISE, Roger; US
JAKOPIN, David; US
ATKINS, William; US
WETHERELL, JR., John; US
JAFFER, David; US
COLLINS, Bryan; US
LAIR, Christophe; US
HOFFMAN, Jean-Paul; US
BLAYLOCK, Richard; US
KIERSZ, Benjamin; US
PEREZ, Robert; US
SCHADE, Jaclyn; US
ZAITLEN, Richard; US
FINKEL, Evan; US
JADDU, Maithreyi; US
ROBINSON, Carmen; US
DAVE, Raju; US
BEDGOOD, Rober; US
DATTA, Madhumita; US
MACAULAY, John; US
SCHICK, Ian; US
MEHOK, Michelle; US
TOTO, Carolyn; US
DOODY, Patrick; US
DAVOUDIAN-MOGHADDAM, Keyvan; US
FUHRER, Rober; US
BANSAL, Manu; US
WEST, JR., William; US
SHU, Haining; US
BEHAR, Victor; US
REYNOLDS, Kecia; US
KING, Grace; US
SHELTON, Barry; US
CHIBIB, Michael; US
NASH, Brian; US
TUCKER, Joshua; US
SHERIFF, Jeffrey; US
HAHN, Peter; US
ZHANG, Ngai; US
MOORE, Steven; US
FORCE, JR., Walker; US
WEINMAN, Sean; US
HAKIM, Souad; US
HEINS, Michael; US
ROSKAMP, Carrie; US
GONZALEZ, Eduardo; US
HAHN, Kirk; US
GRAHAM, Breton; US
MAJDI, Arash; US
Priority Data:
15/009,96829.01.2016US
Title (EN) METHOD AND SYSTEM FOR DISTRIBUTED DEEP MACHINE LEARNING
(FR) PROCÉDÉ ET SYSTÈME POUR UN APPRENTISSAGE MACHINE EN PROFONDEUR DISTRIBUÉ
Abstract:
(EN) The present teaching relates to distributed deep machine learning on a cluster. In one example, a request is received for estimating one or more parameters associated with a machine learning model on a cluster including a plurality of nodes. A set of data is obtained to be used for estimating the one or more parameters. The set of data is divided into a plurality of sub-sets of data, each of which corresponds to one of the plurality of nodes. Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.
(FR) La présente invention concerne un apprentissage machine en profondeur distribué sur un groupe. Dans un exemple, une requête est reçue pour estimer un ou plusieurs paramètres associés à un modèle d'apprentissage machine sur un groupe comprenant une pluralité de nœuds. Un ensemble de données est obtenu pour être utilisé pour estimer le ou les paramètres. L'ensemble de données est divisé en une pluralité de sous-ensembles de données, dont chacun correspond à l'un de la pluralité de nœuds. Chaque sous-ensemble de données est affecté à un nœud correspondant pour estimer des valeurs du ou des paramètres sur la base du sous-ensemble de données. Des valeurs estimées du ou des paramètres obtenus sur la base d'un sous-ensemble correspondant de données affectées au nœud sont reçues à partir de chacun de la pluralité de nœuds. Le ou les paramètres du modèle d'apprentissage machine sont estimés sur la base des valeurs estimées du ou des paramètres générés par au moins certains nœuds parmi la pluralité de nœuds.
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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, 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
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)