http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022222534-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_69108fc12b940ede105638f37f06080b |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-082 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0445 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0481 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate | 2020-03-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a8242fd6bf5c5be4a522cd4e13f18a63 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_72959405f8595731b7990ca0015d3afb http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0cfb982338112b09f8208d2491590648 |
| publicationDate | 2022-07-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | US-2022222534-A1 |
| titleOfInvention | System and method for incremental learning using a grow-and-prune paradigm with neural networks |
| abstract | According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2020104716-A1 |
| priorityDate | 2019-05-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID22978774 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419701332 |
Total number of triples: 24.