Any sort of probabilistic model offers the usual interpretations of the probabilities as an interface. For instance, I can train an LDA topic model, look at the words in the learned topics, pick a topic I’m interested in, then look at that topic’s weighting in each document in order to find relevant documents. More generally, I can train any clustering model, pick a cluster I’m interested in, then look for more things in that cluster. Or if I train a causal model, I can often interpret the learned parameters as estimates of physical interactions in the world. In each case, I’m effectively using the interpretation of the model’s built-in probabilities as an interface.
This is arguably the main advantage of probabilistic models over non-probabilistic models: they come with a fairly reliable, well-understood built-in interface.
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Any sort of probabilistic model offers the usual interpretations of the probabilities as an interface. For instance, I can train an LDA topic model, look at the words in the learned topics, pick a topic I’m interested in, then look at that topic’s weighting in each document in order to find relevant documents. More generally, I can train any clustering model, pick a cluster I’m interested in, then look for more things in that cluster. Or if I train a causal model, I can often interpret the learned parameters as estimates of physical interactions in the world. In each case, I’m effectively using the interpretation of the model’s built-in probabilities as an interface.
This is arguably the main advantage of probabilistic models over non-probabilistic models: they come with a fairly reliable, well-understood built-in interface.
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You might be interested in some of Chris Olah’s work on interpretability. For example, this.
EDIT: Or even just the example of sampling from the latent space of a variational autoencoder should count, I would think.
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