| Summary: | 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). === 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. === by Nick Hynes. === M. Eng.
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