| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53f347c8f604f15767e8e73fec62095a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20 |
| filingDate |
2021-04-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_14c54bdfef23249a705daed11fb503a9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d0f56c5ec89009c31e789c4949c037b2
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5b8c2d20ffd8eeee1c1cd32994cffa69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_cc5c8f574cb81f0b748aa70be3819a25
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5bb38904c16defdc595cba7016ddfb1a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a1f0ecdc1c1d9f8a4cfaa2fe751c02ee
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a27929376e91d94c1aa684b616e594ac |
| publicationDate |
2022-10-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022318684-A1 |
| titleOfInvention |
Sparse ensembling of unsupervised models |
| abstract |
Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models. |
| priorityDate |
2021-04-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |