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Episodic and semantic memory are critical components of the human memorymodel. The theory of complementary learning systems (McClelland et al., 1995)suggests that the compressed representation produced by a serial event(episodic memory) is later restructured to build a more generalized form ofreusable knowledge (semantic memory). In this work we develop a new principledBayesian memory allocation scheme that bridges the gap between episodic andsemantic memory via a hierarchical latent variable model. We take inspirationfrom traditional heap allocation and extend the idea of locally contiguousmemory to the Kanerva Machine, enabling a novel differentiable block allocatedlatent memory. In contrast to the Kanerva Machine, we simplify the process ofmemory writing by treating it as a fully feed forward deterministic process,relying on the stochasticity of the read key distribution to disperseinformation within the memory. We demonstrate that this allocation schemeimproves performance in memory conditional image generation, resulting in newstate-of-the-art conditional likelihood values on binarized MNIST (<=41.58nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presentingcompetitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.

Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory