Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308017838> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W4308017838 abstract "Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine learning models that are competitive on a variety of tasks, it has been unclear how to perform transfer learning for kernel methods. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. In particular, we show that transferring modern kernels trained on large-scale image datasets can result in substantial performance increase as compared to using the same kernel trained directly on the target task. In addition, we show that transfer-learned kernels allow a more accurate prediction of the effect of drugs on cancer cell lines. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws. By providing a simple and effective transfer learning framework for kernel methods, our work enables kernel methods trained on large datasets to be easily adapted to a variety of downstream target tasks." @default.
- W4308017838 created "2022-11-07" @default.
- W4308017838 creator A5008891620 @default.
- W4308017838 creator A5051451341 @default.
- W4308017838 creator A5056082346 @default.
- W4308017838 creator A5078137469 @default.
- W4308017838 date "2022-10-31" @default.
- W4308017838 modified "2023-09-27" @default.
- W4308017838 title "Transfer Learning with Kernel Methods" @default.
- W4308017838 doi "https://doi.org/10.48550/arxiv.2211.00227" @default.
- W4308017838 hasPublicationYear "2022" @default.
- W4308017838 type Work @default.
- W4308017838 citedByCount "0" @default.
- W4308017838 crossrefType "posted-content" @default.
- W4308017838 hasAuthorship W4308017838A5008891620 @default.
- W4308017838 hasAuthorship W4308017838A5051451341 @default.
- W4308017838 hasAuthorship W4308017838A5056082346 @default.
- W4308017838 hasAuthorship W4308017838A5078137469 @default.
- W4308017838 hasBestOaLocation W43080178381 @default.
- W4308017838 hasConcept C111472728 @default.
- W4308017838 hasConcept C114614502 @default.
- W4308017838 hasConcept C119857082 @default.
- W4308017838 hasConcept C122280245 @default.
- W4308017838 hasConcept C12267149 @default.
- W4308017838 hasConcept C134517425 @default.
- W4308017838 hasConcept C136197465 @default.
- W4308017838 hasConcept C138885662 @default.
- W4308017838 hasConcept C140417398 @default.
- W4308017838 hasConcept C150899416 @default.
- W4308017838 hasConcept C154945302 @default.
- W4308017838 hasConcept C162324750 @default.
- W4308017838 hasConcept C187736073 @default.
- W4308017838 hasConcept C2776879701 @default.
- W4308017838 hasConcept C2780451532 @default.
- W4308017838 hasConcept C2780586882 @default.
- W4308017838 hasConcept C33923547 @default.
- W4308017838 hasConcept C41008148 @default.
- W4308017838 hasConcept C74193536 @default.
- W4308017838 hasConcept C75866337 @default.
- W4308017838 hasConceptScore W4308017838C111472728 @default.
- W4308017838 hasConceptScore W4308017838C114614502 @default.
- W4308017838 hasConceptScore W4308017838C119857082 @default.
- W4308017838 hasConceptScore W4308017838C122280245 @default.
- W4308017838 hasConceptScore W4308017838C12267149 @default.
- W4308017838 hasConceptScore W4308017838C134517425 @default.
- W4308017838 hasConceptScore W4308017838C136197465 @default.
- W4308017838 hasConceptScore W4308017838C138885662 @default.
- W4308017838 hasConceptScore W4308017838C140417398 @default.
- W4308017838 hasConceptScore W4308017838C150899416 @default.
- W4308017838 hasConceptScore W4308017838C154945302 @default.
- W4308017838 hasConceptScore W4308017838C162324750 @default.
- W4308017838 hasConceptScore W4308017838C187736073 @default.
- W4308017838 hasConceptScore W4308017838C2776879701 @default.
- W4308017838 hasConceptScore W4308017838C2780451532 @default.
- W4308017838 hasConceptScore W4308017838C2780586882 @default.
- W4308017838 hasConceptScore W4308017838C33923547 @default.
- W4308017838 hasConceptScore W4308017838C41008148 @default.
- W4308017838 hasConceptScore W4308017838C74193536 @default.
- W4308017838 hasConceptScore W4308017838C75866337 @default.
- W4308017838 hasLocation W43080178381 @default.
- W4308017838 hasLocation W43080178382 @default.
- W4308017838 hasLocation W43080178383 @default.
- W4308017838 hasLocation W43080178384 @default.
- W4308017838 hasOpenAccess W4308017838 @default.
- W4308017838 hasPrimaryLocation W43080178381 @default.
- W4308017838 hasRelatedWork W1969163824 @default.
- W4308017838 hasRelatedWork W1983263273 @default.
- W4308017838 hasRelatedWork W2092483655 @default.
- W4308017838 hasRelatedWork W2120149881 @default.
- W4308017838 hasRelatedWork W2407185198 @default.
- W4308017838 hasRelatedWork W2621945803 @default.
- W4308017838 hasRelatedWork W2898882859 @default.
- W4308017838 hasRelatedWork W2963372274 @default.
- W4308017838 hasRelatedWork W4291669689 @default.
- W4308017838 hasRelatedWork W4308017838 @default.
- W4308017838 isParatext "false" @default.
- W4308017838 isRetracted "false" @default.
- W4308017838 workType "article" @default.