This site has been permanently archived. This is a static copy provided by the University of Southampton.
--- abstract: "We discuss the derivation and implementation of convolutional neural networks, followed by an extension which allows one to learn sparse combinations of feature maps. The derivation we present is specific to two-dimensional data and convolutions, but can be extended without much additional effort to an arbitrary number of dimensions. Throughout the discussion, we emphasize\r\nefficiency of the implementation, and give small snippets of MATLAB code to accompany the equations." altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Bouvrie given: Jake honourific: '' lineage: '' date: 2006-11-22 date_type: completed datestamp: 2007-12-10 21:42:07 department: ~ dir: disk0/00/00/58/69 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 5869 fileinfo: /style/images/fileicons/application_pdf.png;/5869/1/cnn_tutorial.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: unpub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'convolutional neural networks, machine vision, machine learning' lastmod: 2011-03-11 08:57:01 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: TRUE publication: ~ publisher: ~ refereed: FALSE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 22 series: ~ source: ~ status_changed: 2007-12-10 21:42:07 subjects: - comp-sci-neural-nets succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Notes on Convolutional Neural Networks type: other userid: 7430 volume: ~