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realized) into 3D volumes or fiber meshes and stored in memory, We introduce a novel realization-minimizing technique that enables physically based rendering of procedural textiles, without the need of full model realizations.
Mercer kernels as used in Support Vector Machines [37] are not sufficient for creating the associative memory we introduce, since our memories also require that all attractors are linearly independent in the feature space.
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To relax the constraint put by the memory space, we introduce the following frequency domain variables, y ̃ ¯ f k and D ¯ i k.
Thus, we introduce memory-bounded parallel A*, a variant of A* algorithm that only uses limited memory.
In this paper, we introduce a memory model, in which its memory attractors do not lie in the input or neural space as in classical models but rather in a feature space with large or infinite dimension.
We introduce shared-memory parallelism in a parallel distributed-memory solver, targeting multi-core architectures.
To incorporate topical correlations into our model, we introduce a memory effect connecting word frequencies across different documents.
We introduce group-based memory deduplication and reprovisioning with several policies based on per-group workload behaviors.
We introduce a limited memory version, L-FCFW, of the Fully-Corrective Frank-Wolfe method with approximate correction, to solve the dual problem.
In this article, we introduce WoMMBAT (working memory modeling using Bayesian analysis techniques), graphical software for estimating capacity using Morey's hierarchical model.
We introduce a bidirectional associative memory (BAM).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com