Understanding Mixture Density Neural Networks Training Ppt
These slides introduce the concept of mixture density neural networks. They explain the two components neural network and mixture model that a mixture density network comprises.
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Slide 1
This slide gives an introduction to Mixture Density Networks. A Mixture Density Network is an interesting model for working on supervised learning issues where a single standard probability distribution cannot represent the target variable. It is created within the basic framework of neural networks and probability theory.
Instructor’s Notes: In essence, an MDN allows you to model a conditional probability distribution p(y|x) as a mixture of distributions, with the individual distributions and mixing coefficients defined by functions of the inputs x.
Slide 2
This slide depicts the two components that a Mixture Density Network is made of. These are a neural network and a mixture model
Instructor’s Notes:
- Neural Network: Any viable architecture that takes the input X and converts it into a set of learned features is a Neural Network (we can think of it as an encoder or backbone)
- Mixture Model: The Mixture Model is a probability distribution model composed of a weighted sum of simpler distributions
Slide 3
This slide shows how a Mixture Density Network looks like. The Mixture Density Network is a feed-forward neural network that determines the parameters of a mixture density model using its outputs.
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