text: 361,247,1369377672082690048,66173851,Jason Ramapuram,"Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory : our recent ICLR 2021 paper in collaboration with Yan Wu & Alexandros Kalousis. <LINK> We propose a novel memory model inspired by traditional heap allocation <LINK> We extend the Kanerva Machine ( <LINK> ) by simplifying the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. <LINK> Sample generation uses stochasticity in a low dimensional key distribution (R^3) which is then used to parameterize a spatial transformer (<LINK>) to read contiguous memory sub-regions. <LINK> Local key perturbations of a trained DMLab maze K++ model induces resultant generations that provide a natural traversal of the maze as observed by scanning the figure below row by row, from left to right. The model was trained with IID inputs! <LINK> A similar behavior is observed for Omniglot generations. <LINK> We also show that iterative inference via the K++ model is robust to various kinds of noise such as salt & pepper, speckle and Poisson noise. <LINK> TLDR: stochasticity in a low dimensional distribution can be leveraged to generate samples by reading contiguous spatial latents from a differentiable memory model! @poolio We use the std Larochelle bin-MNIST with the std splits (code in ICLR supp material). Quick run attached. Lower value reasons: 1. Core stochasticity is in a low dim space (R^3) --&gt; decoder can better reconstruct samples. 2. Memory houses contextual info about other samples. <LINK>",https://arxiv.org/abs/2103.03905,"Episodic and semantic memory are critical components of the human memory model. 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 of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32. ","Kanerva++: extending The Kanerva Machine with differentiable, locally