Publication: Photonic neural networks with random projection kernel optimization
| dc.conference.date | 23 June 2025 - 27 June 2025 | |
| dc.conference.location | Munich | |
| dc.conference.organizer | EPS, Optica and IEEE Photonics Society | |
| dc.contributor.department | Department of Electrical and Electronics Engineering | |
| dc.contributor.kuauthor | Çarpınlıoğlu, Bora | |
| dc.contributor.kuauthor | Teğin, Uğur | |
| dc.contributor.schoolcollegeinstitute | College of Engineering | |
| dc.date.accessioned | 2025-12-31T08:23:18Z | |
| dc.date.available | 2025-12-31 | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Optical computing offers faster processing, lower power use, and parallel computations. Neural networks find promise in optical computing due to dense connectivity and noise resistance, with applications making use of chaos in photonic neural networks [1]. As a prominent technique, linear optical neural networks for large datasets use random projection, reducing high-dimensional data dimensionality [2]. Optical platforms with this method are explored [3], where the challenge of frequent optical-electronic connections is circumvented with numeric designs of scattering environments, but only in the THz band [4]. © 2025 Elsevier B.V., All rights reserved. | |
| dc.description.fulltext | Yes | |
| dc.description.harvestedfrom | Manual | |
| dc.description.indexedby | Scopus | |
| dc.description.publisherscope | International | |
| dc.description.readpublish | N/A | |
| dc.description.sponsoredbyTubitakEu | TÜBİTAK | |
| dc.identifier.doi | 10.1109/CLEO/EUROPE-EQEC65582.2025.11110168 | |
| dc.identifier.eissn | 2833-1052 | |
| dc.identifier.embargo | No | |
| dc.identifier.grantno | 123E308 | |
| dc.identifier.isbn | 9798331512521 | |
| dc.identifier.quartile | N/A | |
| dc.identifier.scopus | 2-s2.0-105016219592 | |
| dc.identifier.uri | https://doi.org/10.1109/CLEO/EUROPE-EQEC65582.2025.11110168 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14288/31717 | |
| dc.keywords | Large datasets | |
| dc.keywords | Numerical methods | |
| dc.keywords | Optical data processing | |
| dc.keywords | Photonics | |
| dc.keywords | Quantum electronics | |
| dc.keywords | Transparent optical networks | |
| dc.keywords | Fast processing | |
| dc.keywords | Kernel optimizations | |
| dc.keywords | Linear optical | |
| dc.keywords | Low power | |
| dc.keywords | Neural-networks | |
| dc.keywords | Noise resistance | |
| dc.keywords | Optical neural networks | |
| dc.keywords | Optical- | |
| dc.keywords | Parallel computation | |
| dc.keywords | Random projections | |
| dc.keywords | Optimization | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.affiliation | Koç University | |
| dc.relation.collection | Koç University Institutional Repository | |
| dc.relation.ispartof | 2025 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2025 | |
| dc.relation.openaccess | Yes | |
| dc.rights | CC BY-NC-ND (Attribution-NonCommercial-NoDerivs) | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.title | Photonic neural networks with random projection kernel optimization | |
| dc.type | Conference Proceeding | |
| dspace.entity.type | Publication | |
| person.familyName | Çarpınlıoğlu | |
| person.familyName | Teğin | |
| person.givenName | Bora | |
| person.givenName | Uğur | |
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