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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2020,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Datasets from single-molecule experiments often reflect a large variety of molecular behaviour. The exploration of such datasets can be challenging, especially if knowledge about the data is limited and <jats:italic>a priori<\/jats:italic> assumptions about expected data characteristics are to be avoided. Indeed, searching for pre-defined signal characteristics is sometimes useful, but it can also lead to information loss and the introduction of expectation bias. Here, we demonstrate how Transfer Learning-enhanced dimensionality reduction can be employed to identify and quantify hidden features in single-molecule charge transport data, in an unsupervised manner. Taking advantage of open-access neural networks trained on millions of seemingly unrelated image data, our results also show how Deep Learning methodologies can readily be employed, even if the amount of problem-specific, \u2018own\u2019 data is limited.<\/jats:p>","DOI":"10.1088\/2632-2153\/aba6f2","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T22:15:14Z","timestamp":1595024114000},"page":"035013","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Unsupervised classification of single-molecule data with autoencoders and transfer learning"],"prefix":"10.1088","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1770-8139","authenticated-orcid":false,"given":"Anton","family":"Vladyka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6085-3206","authenticated-orcid":false,"given":"Tim","family":"Albrecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,8,21]]},"reference":[{"key":"mlstaba6f2bib1","doi-asserted-by":"publisher","DOI":"10.1038\/natrevmats.2017.8","article-title":"Atomic force microscopy-based characterization and design of biointerfaces","volume":"2","author":"Alsteens","year":"2017","journal-title":"Nat. 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