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Systolic arrays (< wavefront processors), first described by H. T. Kung and Charles E. Leiserson are an example of MISD architecture. In a typical systolic array, parallel input data flows through a network of hard-wired processor nodes, resembling the human brain which combine, process, merge or sort the input data into a derived result.
A video or an image sequence represented as a third-order tensor of column x row x time for multilinear subspace learning. Multilinear subspace learning is an approach for disentangling the causal factor of data formation and performing dimensionality reduction.
Soar [1] is a cognitive architecture, [2] originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.. The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of ...
Hermes Project: C++/Python library for rapid prototyping of space- and space-time adaptive hp-FEM solvers. IML++ is a C++ library for solving linear systems of equations, capable of dealing with dense, sparse, and distributed matrices. IT++ is a C++ library for linear algebra (matrices and vectors), signal processing and communications ...
The Linda model provides a distributed shared memory, known as a tuple space because its basic addressable unit is a tuple, an ordered sequence of typed data objects; specifically in Linda, a tuple is a sequence of up to 16 typed fields enclosed in parentheses". The tuple space is "logically shared by processes" which are referred to as workers ...
Their approach was to regard each molecule as a labeled bag, and all the alternative low-energy shapes of that molecule as instances in the bag, without individual labels. Thus formulating multiple-instance learning. Solution to the multiple instance learning problem that Dietterich et al. proposed is the axis-parallel rectangle (APR) algorithm ...
In Auguin’s SPMD model, the same (parallel) task (“same program”) is executed on different (SIMD) processors (“operating in lock-step mode” [1] acting on a part (“slice”) of the data-vector. Specifically, in their 1985 paper [2] (and similarly in [3] [1]) is stated: “we consider the SPMD (Single Program, Multiple Data) operating ...
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.