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2017 GTC San Jose

S7355 - Learning Large-Scale Multimodal Data Streams: Ranking, Mining, and Machine Comprehension

Session Speakers
Session Description

We'll demonstrate how to design the end-to-end neural networks for leveraging large-scale multimodal data streams for ranking (recommendation), mining human behaviors/interests, and machine comprehension jointly from different modalities such as images, videos, audios, and 3D models. We'll present effective neural networks for considering both sequential (temporal) and spatial (convolutional) variations and numerous strategies for cross-modal learning. We'll show how to tackle the cross-domain problems (for example, images vs. 3D models, audio vs. text), how to leverage freely available web data for training in a semi-supervised or unsupervised manner. We'll describe breakthroughs in 3D model retrieval, human activities understanding from social media, listening comprehension test, and more.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Signal and Audio Processing
Cloud Services Media & Entertainment
50 minutes
Session Schedule