| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_7542cdde926ccc0ad7b27ea92c083088 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2414
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-13
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2019-12-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c450d056a5d6e8a9b19f98c2fe36f853
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f89f5f9a52b243948bb70cbf4e77552a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c5af9889357db1b818bb4cf6a73c0b0c |
| publicationDate |
2020-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2020149992-A1 |
| titleOfInvention |
Modelling ordinary differential equations using a variational auto encoder |
| abstract |
A computer-implemented method comprising: from each of multiple trials, obtaining a respective series of observations y(t) of a subject over time t; using a variational auto encoder to model an ordinary differential equation, ODE, wherein the variational auto encoder comprises an encoder for encoding the observations into a latent vector z and a decoder for decoding the latent vector, the encoder comprising a first neural network and the decoder comprising one or more second neural networks, wherein the ODE as modelled by the decoder has a state x(t) representing one or more physical properties of the subject which result in the observations y, and the decoder models a rate of change of x with respect to time t as a function f of at least x and z: dx/dt = f(x, z); and operating the variational auto encoder to learn the function f based on the obtained observations y. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113486952-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022060235-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113486952-B |
| priorityDate |
2019-01-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |