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Found 6 papers, 1 papers with code
Computational complexity reduction of deep neural networks
Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation.
E$^2$CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning
Moreover, if there are no limitations on the training time budget, E$^2$CM can be combined with an existing early exit scheme to boost the latter's performance, achieving a better trade-off between computational cost and network accuracy.
Genetic optimization algorithms applied toward mission computability models
Genetic algorithms are modeled after the biological evolutionary processes that use natural selection to select the best species to survive.
Solving machine learning optimization problems using quantum computers
We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms.
Optimization problems with low SWaP tactical Computing
In a resource-constrained, contested environment, computing resources need to be aware of possible size, weight, and power (SWaP) restrictions.
Complexity and mission computability of adaptive computing systems
There is a subset of computational problems that are computable in polynomial time for which an existing algorithm may not complete due to a lack of high performance technology on a mission field.
