Inverse Predictivity
The ability to infer latent factors from features, used to diagnose the geometric structure of representations.
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The authors propose three asymmetric diagnostic metrics to distinguish linear world models from kernel machines: reverse predictiveness, spectral concentration, and out-of-distribution generalization.
In a linear world model, reverse prediction should be symmetric with forward prediction, whereas in a kernel machine, reverse prediction is significantly weaker than forward prediction.
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The authors propose three asymmetric diagnostic metrics to distinguish linear world models from kernel machines: reverse predictiveness, spectral concentration, and out-of-distribution generalization.
50%UnverifiedIn a linear world model, reverse prediction should be symmetric with forward prediction, whereas in a kernel machine, reverse prediction is significantly weaker than forward prediction.
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