The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...
A new model shows that a range of visual illusions in humans can be explained as rational inferences about the odds that a motion stimulus on the retina results from a particular real-world source.
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