Transcription of Multimodal Deep Learning - People | MIT CSAIL
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Multimodal Deep Learning - People | MIT CSAIL
Multimodal Deep Learning Jiquan Ngiam1 Aditya Khosla1 Mingyu Kim1 Juhan Nam1 Honglak Lee2 Andrew Y. Ng1 1. Computer Science Department, Stanford University, Stanford, CA 94305, USA. 2. Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA. Abstract mation on the place of articulation and muscle move- ments (Summerfield, 1992) which can often help to dis- Deep networks have been successfully applied ambiguate between speech with similar acoustics ( , to unsupervised feature Learning for single the unvoiced consonants /p/ and /k/ ). modalities ( , text, images or audio). In this work, we propose a novel application of Multimodal Learning involves relating information deep networks to learn features over multiple from multiple sources. For example, images and 3-d modalities. We present a series of tasks for depth scans are correlated at first-order as depth dis- Multimodal Learning and show how to train continuities often manifest as strong edges in images.
Multimodal Deep Learning Jiquan Ngiam1 jngiam@cs.stanford.edu Aditya Khosla1 aditya86@cs.stanford.edu Mingyu Kim1 minkyu89@cs.stanford.edu Juhan Nam1 juhan@ccrma.stanford.edu Honglak Lee2 honglak@eecs.umich.edu Andrew Y. Ng1 ang@cs.stanford.edu 1 Computer Science Department, Stanford University, Stanford, CA …
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