Global ETD Search
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Showing 1 to 10 of 10 for “"Exploiting Sparsity"”.
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Exploiting sparsity for machine learning in big data
… volume and complexity of such big data, however, sparsity has been recently studied to tackle this challenge. With reasonable assumptions and effective utilization of sparsity, we can learn models that are simpler, more efficient and robust to noise. The goal of this dissertation is studying and …
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Location of stable and unstable equilibrium configurations using a model trust region quasi-Newton method and tunnelling
… equilibrium paths with turning points and exploiting sparsity of the Jacobian matrix at the same time. A quasi-Newton method in conjunction with a deflation technique is proposed here as an alternative to the hybrid method. The proposed method not only exploits sparsity and symmetry, but …
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Microarchitecture Categorization and Pre-RTL Analytical Modeling for Sparse Tensor Accelerators
Specialized microarchitectures for exploiting sparsity have been critical to the design of sparse tensor accelerators. Sparseloop introduced the Sparse Acceleration Feature (SAF) abstraction, which unifies prior work on sparse tensor accelerators into a taxonomy of sparsity optimizations. …
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Accelerated sampling of energy landscapes
… procedure. For the larger systems, exploiting sparsity reduces the computational cost by factors of 10 to 30. The acceleration of these computational energy landscape methods opens up the possibility of investigating much larger and more complex systems than previously accessible. A …
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Energy efficient accelerators for autonomous navigation in miniaturized robots
… Parallelism, rescheduling, resource sharing, exploiting sparsity, and image compression are applied to overcome the high dimensionality of the problem, resulting in 4.1 memory size reduction, and enabling full integration. Navion can adapt to different environments to maximize accuracy, …
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A Cognitive Radio Compressive Sensing Framework
… these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band …
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Mathematical Imaging Tools in Cancer Research - From Mitosis Analysis to Sparse Regularisation
… which circular objects should be enhanced, (ii) exploiting sparsity of first-order derivatives in combination with the linear circular Hough transform operation. Furthermore, (iii) we present a new unified higher-order derivative-type regularisation functional enforcing sparsity of a vector field …
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Adaptive sampling for spatial prediction in wireless sensor networks
… (GMRF) is utilized to model the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of a greedy algorithm to solve the resulting optimization is deterministic even with …
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Theoretical guarantees and complexity reduction in information planning
… MI in Gaussian models by taking advantage of sparsity in the measurement process; and (iii) we propose a variant of belief propagation that is suitable for adaptive inference settings. In the first part, we present conditions under which open-loop is equivalent to closed-loop information …