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1. WO2020198566 - SYSTEMS AND METHODS FOR ANALYZING COMPUTATIONAL ARCHITECTURES

Publication Number WO/2020/198566
Publication Date 01.10.2020
International Application No. PCT/US2020/025159
International Filing Date 27.03.2020
IPC
G06K 9/62 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
G06K 9/64 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
64using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix
G06K 9/66 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
64using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix
66references adjustable by an adaptive method, e.g. learning
G06N 3/06 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
G06N 3/063 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
063using electronic means
G06N 3/08 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
Applicants
  • F0CAL, INC. [US]/[US]
Inventors
  • ROSSA, Brian
Agents
  • MORRIS, James, H.
  • AMUNDSEN, Eric, L.
  • BAKER, C., Hunter
  • ATTISHA, Michael, J.
  • ALAM, Saad
Priority Data
62/824,97527.03.2019US
62/851,01721.05.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEMS AND METHODS FOR ANALYZING COMPUTATIONAL ARCHITECTURES
(FR) SYSTÈMES ET PROCÉDÉS D'ANALYSE D'ARCHITECTURES INFORMATIQUES
Abstract
(EN)
Systems and methods for estimating a random distribution for an overall metric for a composite node, the composite node comprising a plurality of nodes. For each data atom of a plurality of data atoms being input to the composite node, and for each node of the plurality of nodes, at least one value may be generated for a per-node metric with respect to the data atom. A value for the overall metric with respect to the data atom may be generated based on the per-node metric values of the plurality of nodes. At least one parameter of the random distribution for the overall metric for the composite node may be estimated based on the overall metric values with respect to the plurality of data atoms.
(FR)
L'invention concerne des systèmes et des procédés d'estimation d'une répartition aléatoire pour une mesure globale pour un nœud composite, le nœud composite comprenant une pluralité de nœuds. Pour chaque atome de données d'une pluralité d'atomes de données qui sont entrés dans le nœud composite, et pour chaque nœud de la pluralité de nœuds, au moins une valeur peut être générée pour une mesure par nœud par rapport à l'atome de données. Une valeur pour la mesure globale par rapport à l'atome de données peut être générée sur la base des valeurs de mesures par nœud de la pluralité de nœuds. Au moins un paramètre de la répartition aléatoire pour la mesure globale pour le nœud composite peut être estimé sur la base des valeurs de mesures globales par rapport à la pluralité d'atomes de données.
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