text:   block allocated latent memory",8,"['Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory : our recent ICLR 2021 paper in collaboration with Yan Wu &amp; Alexandros Kalousis.\n\n<LINK>\n\nWe propose a novel memory model inspired by traditional heap allocation <LINK>', 'We extend the Kanerva Machine ( https://t.co/ZM8vnC9qow ) 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. https://t.co/utE7eZtPIE', 'Sample generation uses stochasticity in a low dimensional key distribution (R^3) which is then used to parameterize a spatial transformer (https://t.co/L0SOw7C2rI) to read contiguous memory sub-regions. https://t.co/6J6Sg4RkLP', '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! https://t.co/KnrkH378Zi', 'A similar behavior is observed for Omniglot generations. https://t.co/YJcivStLvd', 'We also show that iterative inference via the K++ model is robust to various kinds of noise such as salt &amp; pepper, speckle and Poisson noise. https://t.co/FklOLb2fMa', '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:\n1. Core stochasticity is in a low dim space (R^3) --&gt; decoder can better reconstruct samples. \n2. Memory houses contextual info about other samples. https://t.co/iQdD6wt0cd']",21,03,1638