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GTC ON-DEMAND

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Abstract:
We discuss the basic concepts of Bayesian deep learning will be introduced with a hands-on tutorial that walks through several example applications using ZhuSuan (https://github.com/thu-ml/zhusuan). We'll start with simpler models like Bayesian logistic regression, and then proceed to deeper ones like Bayesian neural networks (BNN) and variational autoencoders (VAE). Learn how to use Bayesian methods to capture uncertainty of deep learning, including modeling the data distribution, calibrating the confidence of outputs, and smoothing predictions to prevent overfitting. Real problems (e.g. regression, image generation, semi-supervised classification) will be used to illustrate the models.
We discuss the basic concepts of Bayesian deep learning will be introduced with a hands-on tutorial that walks through several example applications using ZhuSuan (https://github.com/thu-ml/zhusuan). We'll start with simpler models like Bayesian logistic regression, and then proceed to deeper ones like Bayesian neural networks (BNN) and variational autoencoders (VAE). Learn how to use Bayesian methods to capture uncertainty of deep learning, including modeling the data distribution, calibrating the confidence of outputs, and smoothing predictions to prevent overfitting. Real problems (e.g. regression, image generation, semi-supervised classification) will be used to illustrate the models.  Back
 
Topics:
Deep Learning & AI Frameworks, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8593
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