{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/113147"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/113147","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Representation learning of recipes","abstract":"This work introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and publicly released -- as part of this work. Their representations are then embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings.","abstract_html":"This work introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and publicly released -- as part of this work. Their representations are then embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings.","abstract_has_math":false,"creators":["Hynes, Nick (Nick I.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Antonio Torralba."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:09Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/113147","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Antonio Torralba."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Hynes, Nick (Nick I.)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-12T20:59:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-12T20:59:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/113147"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 41-44)."]},{"key":"dc:description.abstract","label":"Abstract","values":["This work introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and publicly released -- as part of this work. Their representations are then embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Representation learning of recipes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Antonio Torralba."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Hynes, Nick (Nick I.)"],"dc:date.accessioned":["2018-01-12T20:59:43Z"],"dc:date.available":["2018-01-12T20:59:43Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 41-44)."],"dc:description.abstract":["This work introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and publicly released -- as part of this work. Their representations are then embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/113147"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Representation learning of recipes"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:09Z"}