FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2161105493> ?p ?o ?g. }

• W2161105493 endingPage "1805" @default.
• W2161105493 startingPage "1797" @default.
• W2161105493 abstract "Nonparametric estimation of the conditional distribution of a response given high-dimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dictionary learning model, which expresses the conditional response density as a convex combination of dictionary densities, with the densities used and their weights dependent on the path through a tree decomposition of the feature space. A fast graph partitioning algorithm is applied to obtain the tree decomposition, with Bayesian methods then used to adaptively prune and average over different sub-trees in a soft probabilistic manner. The algorithm scales efficiently to approximately one million features. State of the art predictive performance is demonstrated for toy examples and two neuroscience applications including up to a million features." @default.
• W2161105493 created "2016-06-24" @default.
• W2161105493 creator A5002768909 @default.
• W2161105493 creator A5065441417 @default.
• W2161105493 creator A5080041744 @default.
• W2161105493 date "2013-12-05" @default.
• W2161105493 modified "2023-09-24" @default.
• W2161105493 title "Multiscale Dictionary Learning for Estimating Conditional Distributions" @default.
• W2161105493 cites W1581352608 @default.
• W2161105493 cites W174209614 @default.
• W2161105493 cites W1963745470 @default.
• W2161105493 cites W1975427826 @default.
• W2161105493 cites W1990134753 @default.
• W2161105493 cites W1991681888 @default.
• W2161105493 cites W2005931557 @default.
• W2161105493 cites W2005963104 @default.
• W2161105493 cites W2017791169 @default.
• W2161105493 cites W2023989213 @default.
• W2161105493 cites W2059029763 @default.
• W2161105493 cites W2060379981 @default.
• W2161105493 cites W2063046373 @default.
• W2161105493 cites W2070232376 @default.
• W2161105493 cites W2090054001 @default.
• W2161105493 cites W2096610373 @default.
• W2161105493 cites W2100634972 @default.
• W2161105493 cites W2108907664 @default.
• W2161105493 cites W2112136981 @default.
• W2161105493 cites W2119479037 @default.
• W2161105493 cites W2124983283 @default.
• W2161105493 cites W2132337600 @default.
• W2161105493 cites W2150884987 @default.
• W2161105493 cites W2151572595 @default.
• W2161105493 cites W2156295356 @default.
• W2161105493 cites W2160455305 @default.
• W2161105493 cites W2329148842 @default.
• W2161105493 doi "https://doi.org/10.6084/m9.figshare.1284161.v1" @default.
• W2161105493 hasPublicationYear "2013" @default.
• W2161105493 type Work @default.
• W2161105493 sameAs 2161105493 @default.
• W2161105493 citedByCount "1" @default.
• W2161105493 countsByYear W21611054932019 @default.
• W2161105493 crossrefType "proceedings-article" @default.
• W2161105493 hasAuthorship W2161105493A5002768909 @default.
• W2161105493 hasAuthorship W2161105493A5065441417 @default.
• W2161105493 hasAuthorship W2161105493A5080041744 @default.
• W2161105493 hasConcept C102366305 @default.
• W2161105493 hasConcept C105795698 @default.
• W2161105493 hasConcept C107673813 @default.
• W2161105493 hasConcept C11413529 @default.
• W2161105493 hasConcept C119857082 @default.
• W2161105493 hasConcept C132525143 @default.
• W2161105493 hasConcept C138885662 @default.
• W2161105493 hasConcept C153180895 @default.
• W2161105493 hasConcept C154945302 @default.
• W2161105493 hasConcept C203776342 @default.
• W2161105493 hasConcept C2776401178 @default.
• W2161105493 hasConcept C33923547 @default.
• W2161105493 hasConcept C41008148 @default.
• W2161105493 hasConcept C41895202 @default.
• W2161105493 hasConcept C43517604 @default.
• W2161105493 hasConcept C43555835 @default.
• W2161105493 hasConcept C57830394 @default.
• W2161105493 hasConcept C70501317 @default.
• W2161105493 hasConcept C80444323 @default.
• W2161105493 hasConcept C83665646 @default.
• W2161105493 hasConceptScore W2161105493C102366305 @default.
• W2161105493 hasConceptScore W2161105493C105795698 @default.
• W2161105493 hasConceptScore W2161105493C107673813 @default.
• W2161105493 hasConceptScore W2161105493C11413529 @default.
• W2161105493 hasConceptScore W2161105493C119857082 @default.
• W2161105493 hasConceptScore W2161105493C132525143 @default.
• W2161105493 hasConceptScore W2161105493C138885662 @default.
• W2161105493 hasConceptScore W2161105493C153180895 @default.
• W2161105493 hasConceptScore W2161105493C154945302 @default.
• W2161105493 hasConceptScore W2161105493C203776342 @default.
• W2161105493 hasConceptScore W2161105493C2776401178 @default.
• W2161105493 hasConceptScore W2161105493C33923547 @default.
• W2161105493 hasConceptScore W2161105493C41008148 @default.
• W2161105493 hasConceptScore W2161105493C41895202 @default.
• W2161105493 hasConceptScore W2161105493C43517604 @default.
• W2161105493 hasConceptScore W2161105493C43555835 @default.
• W2161105493 hasConceptScore W2161105493C57830394 @default.
• W2161105493 hasConceptScore W2161105493C70501317 @default.
• W2161105493 hasConceptScore W2161105493C80444323 @default.
• W2161105493 hasConceptScore W2161105493C83665646 @default.
• W2161105493 hasLocation W21611054931 @default.
• W2161105493 hasOpenAccess W2161105493 @default.
• W2161105493 hasPrimaryLocation W21611054931 @default.
• W2161105493 hasRelatedWork W1790031733 @default.
• W2161105493 hasRelatedWork W1790658179 @default.
• W2161105493 hasRelatedWork W1834031794 @default.
• W2161105493 hasRelatedWork W1909878079 @default.
• W2161105493 hasRelatedWork W1919402970 @default.
• W2161105493 hasRelatedWork W2054755313 @default.
• W2161105493 hasRelatedWork W2119224902 @default.
• W2161105493 hasRelatedWork W2146792286 @default.
• W2161105493 hasRelatedWork W2162413715 @default.
• W2161105493 hasRelatedWork W2265943708 @default.

• next