Michael O. Vertolli

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A cognitive model of the visual imagination will produce “incoherent” results when it adds elements to an imagined scene that come from different contexts (e.g., “computer” and “cheese” with “mouse”). We approach this problem with a model that infers coherence relations from co-occurrence probabilities of labels in images. We show that this algorithm’s(More)
We describe the overall theory of the SOILIE model of the human imagination. In this description, we outline cognitive capacities for learning and storage, image component selection and placement, as well as analogical reasoning. The guiding theory behind SOILIE is that visual imagination is constrained by regularities in visual memories.
We propose a training and evaluation approach for autoencoder Generative Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative Adversarial Network (BEGAN), based on methods from the image quality assessment literature. Our approach explores a multidimensional evaluation criterion that utilizes three distance functions: an l1 score,(More)
We propose a large-scale system, with minimal global topological structure, no local internal structure, and a simple online biologically plausible local learning rule that captures supervised learning in the barn owl. We outline how our computational model corresponds to both the underlying neuroscience and the experimental paradigm used in the relevant(More)
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