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Found 3 papers, 3 papers with code
Relative representations for cognitive graphs
Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive "relative representations" with comparable representational power to their "absolute" counterparts, and which are nearly identical across models trained on similar data distributions.
Capsule Networks as Generative Models
Capsule networks are a neural network architecture specialized for visual scene recognition.
Efficient search of active inference policy spaces using k-means
We develop an approach to policy selection in active inference that allows us to efficiently search large policy spaces by mapping each policy to its embedding in a vector space.
