http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CA-3165790-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_861b9df9f1daea7c450118ace4144dd2 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-2457 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N5-02 |
| filingDate | 2021-01-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a36ee653bc57d7acfb7e37f120a4079b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_66ec926789dab50640e0f9311cf25402 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fdb84fde236ce8997e4ed9adefaa9803 |
| publicationDate | 2021-08-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CA-3165790-A1 |
| titleOfInvention | Methods and systems for dynamically generating a plurality of machine learning systems during processing of a user data set |
| abstract | A method for dynamically generating a plurality of machine learning models for processing a user data set includes receiving, by a machine learning engine, a user-specified data set and a user-specified task. The machine learning engine analyzes at least one characteristic of the user-specified data set and task. The machine learning engine selects a plurality of encoders based upon the analysis and directs each to encode the user-specified data set. The machine learning engine generates a first machine learning model for processing the user-specified data set, based upon the at least one characteristic of the user data set and of the task. The machine learning engine directs the first machine learning model to generate a first output. The machine learning engine generates, trains, and executes a second machine learning model based upon the at least one characteristic of the user-specified data set and of the user-specified task. |
| priorityDate | 2020-01-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 26.