Matches in SemOpenAlex for { <https://semopenalex.org/work/W3129928770> ?p ?o ?g. }
Showing items 1 to 73 of
73
with 100 items per page.
- W3129928770 abstract "Advances in scientific fields including drug discovery or material design are accompanied by numerous trials and errors. However, generally only representative experimental results are reported. Because of this reporting bias, the distribution of labeled result data can deviate from their true distribution. A regression model can be erroneous if it is built on these skewed data. In this work, we propose a new approach to improve the accuracy of regression models that are trained using a skewed dataset. The method forces the regression outputs to follow the true distribution; the forcing algorithm regularizes the regression results while keeping the information of the training data. We assume the existence of enough unlabeled data that follow the true distribution, and that the true distribution can be roughly estimated from domain knowledge or a few samples. During training neural networks to generate a regression model, an adversarial network is used to force the distribution of predicted values to follow the estimated ‘true’ distribution. We evaluated the proposed approach on four real-world datasets (pLogP, Diamond, House, Elevators). In all four datasets, the proposed approach reduced the root mean squared error of the regression by around 55 percent to 75 percent compared to regression models without adjustment of the distribution." @default.
- W3129928770 created "2021-03-01" @default.
- W3129928770 creator A5017714788 @default.
- W3129928770 creator A5023852445 @default.
- W3129928770 creator A5037007563 @default.
- W3129928770 creator A5041980056 @default.
- W3129928770 creator A5058941147 @default.
- W3129928770 date "2021-05-04" @default.
- W3129928770 modified "2023-09-28" @default.
- W3129928770 title "Semi-supervised regression with skewed data via adversarially forcing the distribution of predicted values" @default.
- W3129928770 hasPublicationYear "2021" @default.
- W3129928770 type Work @default.
- W3129928770 sameAs 3129928770 @default.
- W3129928770 citedByCount "0" @default.
- W3129928770 crossrefType "journal-article" @default.
- W3129928770 hasAuthorship W3129928770A5017714788 @default.
- W3129928770 hasAuthorship W3129928770A5023852445 @default.
- W3129928770 hasAuthorship W3129928770A5037007563 @default.
- W3129928770 hasAuthorship W3129928770A5041980056 @default.
- W3129928770 hasAuthorship W3129928770A5058941147 @default.
- W3129928770 hasConcept C105795698 @default.
- W3129928770 hasConcept C110121322 @default.
- W3129928770 hasConcept C119857082 @default.
- W3129928770 hasConcept C120068334 @default.
- W3129928770 hasConcept C124101348 @default.
- W3129928770 hasConcept C134306372 @default.
- W3129928770 hasConcept C152877465 @default.
- W3129928770 hasConcept C154945302 @default.
- W3129928770 hasConcept C33923547 @default.
- W3129928770 hasConcept C41008148 @default.
- W3129928770 hasConcept C48921125 @default.
- W3129928770 hasConcept C57381214 @default.
- W3129928770 hasConcept C83546350 @default.
- W3129928770 hasConceptScore W3129928770C105795698 @default.
- W3129928770 hasConceptScore W3129928770C110121322 @default.
- W3129928770 hasConceptScore W3129928770C119857082 @default.
- W3129928770 hasConceptScore W3129928770C120068334 @default.
- W3129928770 hasConceptScore W3129928770C124101348 @default.
- W3129928770 hasConceptScore W3129928770C134306372 @default.
- W3129928770 hasConceptScore W3129928770C152877465 @default.
- W3129928770 hasConceptScore W3129928770C154945302 @default.
- W3129928770 hasConceptScore W3129928770C33923547 @default.
- W3129928770 hasConceptScore W3129928770C41008148 @default.
- W3129928770 hasConceptScore W3129928770C48921125 @default.
- W3129928770 hasConceptScore W3129928770C57381214 @default.
- W3129928770 hasConceptScore W3129928770C83546350 @default.
- W3129928770 hasLocation W31299287701 @default.
- W3129928770 hasOpenAccess W3129928770 @default.
- W3129928770 hasPrimaryLocation W31299287701 @default.
- W3129928770 hasRelatedWork W1532315365 @default.
- W3129928770 hasRelatedWork W1548956039 @default.
- W3129928770 hasRelatedWork W183358878 @default.
- W3129928770 hasRelatedWork W2020510022 @default.
- W3129928770 hasRelatedWork W2083025062 @default.
- W3129928770 hasRelatedWork W2213395179 @default.
- W3129928770 hasRelatedWork W2245241381 @default.
- W3129928770 hasRelatedWork W2296041297 @default.
- W3129928770 hasRelatedWork W2402197802 @default.
- W3129928770 hasRelatedWork W2592176213 @default.
- W3129928770 hasRelatedWork W2616198738 @default.
- W3129928770 hasRelatedWork W2616915261 @default.
- W3129928770 hasRelatedWork W2792409724 @default.
- W3129928770 hasRelatedWork W2906432502 @default.
- W3129928770 hasRelatedWork W2954681975 @default.
- W3129928770 hasRelatedWork W3084814224 @default.
- W3129928770 hasRelatedWork W3123840863 @default.
- W3129928770 hasRelatedWork W3133721416 @default.
- W3129928770 hasRelatedWork W3170889573 @default.
- W3129928770 hasRelatedWork W861962728 @default.
- W3129928770 isParatext "false" @default.
- W3129928770 isRetracted "false" @default.
- W3129928770 magId "3129928770" @default.
- W3129928770 workType "article" @default.