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GTC ON-DEMAND

Presentation
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Abstract:
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.
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.  Back
 
Topics:
Artificial Intelligence and Deep Learning, Signal and Audio Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7355
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