Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 2173 for “"sparse"”.
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Colouring sparse graphs
Contains fulltext : 208472.pdf (Publisher’s version ) (Open Access)
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Sparse Grid Interpolation
… the implementation. Recently, the technique of sparse grids has been introduced to signi cantly reduce the cost to approximate high-dimensional functions under certain regularity conditions. In this thesis, we present the classical sparse grid where the problem is discretized and solved on a …
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Sparse Grid Interpolation
… the implementation. Recently, the technique of sparse grids has been introduced to signi cantly reduce the cost to approximate high-dimensional functions under certain regularity conditions. In this thesis, we present the classical sparse grid where the problem is discretized and solved on a …
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Sparse matrix spaces
… zero, or an arbitrary real number. We call them sparse matrix spaces, abbreviated SMS, and examine under what conditions they contain matrices for which all eigenvalues have strictly negative real parts. We call an SMS with this property stable. We start by reviewing the necessary background from …
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Tardigrade: A Hardware Accelerator for Sparse Matrix Multiplication and Sparse Convolution
Sparse matrix-sparse matrix multiplication (SpMSpM) and sparse convolution are critical primitive operations for scientific computing and deep learning. Prior work has proposed accelerators for each of these primitives, but these systems are often specialized to run either SpMSpM or sparse …
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Sparse Kernel feature extraction
… recent work has been more focused and considers sparser formulations which potentially have improved generalisation. However, sparsity is not always efficiently implemented and frequently requires complex optimisation routines. Furthermore, one often does not have a direct control on the sparsity …
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Gaussian processes:iterative sparse approximations
… Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an …
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Domination in Sparse Graphs
Given a partition pi of V (G) with parts ( V1, V2, ... , V t). A pi-dominating set B is a dominating set that is the union of parts of pi. The pi-domination number gamma(G,pi) of G is the size of a smallest pi-dominating set. If each Vi in pi has size at most 2, we call pi a coupling of G and say …
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On sparse mirror descent
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Deformation-invariant sparse coding
Sparse coding represents input signals each as a sparse linear combination of a set of basis or dictionary elements where sparsity encourages representing each input signal with a few of the most indicative dictionary elements. In this thesis, we extend sparse coding to allow dictionary elements to …
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Sparse tensor algebra compilation
This dissertation shows how to compile any sparse tensor algebra expression to CPU and GPU code that matches the performance of hand-optimized implementations. A tensor algebra expression is sparse if at least one of its tensor operands is sparse, and a tensor is sparse if most of its values are …
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Sparse Matrix Belief Propagation
We propose sparse-matrix belief propagation, which executes loopy belief propagation in Markov random fields by replacing indexing over graph neighborhoods with sparse-matrix operations. This abstraction allows for seamless integration with optimized sparse linear algebra libraries, including those …
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Sparse Approximation In Banach Spaces
<p>The sparse approximation problems ask for complete recovery of functions in a given space that are supported by few of the elements of a system of generators for the space or for approximate recovery that involves a limited number of generators.</p> <p>Traditionally, these problems have been …
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Linear Orderings of Sparse Graphs
… is known about their structure and properties on sparser graphs. There are also only few approximative algorithms that give performance guarantees especially for graphs with bounded vertex degree. This thesis fills this gap in multiple respects: We establish necessary conditions for a linear …
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Reducing communication in sparse solvers
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the performance and scalability of these operations is limited by irregular point-to-point communication. Multiple methods are investigated throughout this dissertation for reducing the cost associated with …
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Sparse Expansion and Neuronal Disentanglement
… of an LLM by expanding it into a mixture of sparse experts, where each expert is a copy of the same weights and one-shot pruned for a specific cluster of input values. We call this approach Sparse Expansion. We show that for models like Llama 2 7B, as we increase the number of experts, Sparse …
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Sparse recovery and Fourier sampling
… decade a broad literature has arisen studying sparse recovery, the estimation of sparse vectors from low dimensional linear projections. Sparse recovery has a wide variety of applications such as streaming algorithms, image acquisition, and disease testing. A particularly important subclass of …
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Sparse and Structured Tensor Programming
… The looplets language decomposes loops over sparse and structured formats hierarchically. This decomposition simplifies compilation, allowing us to capture key mathematical properties (such as x∗0 = 0, which motivates sparsity) with simple term rewriting. Building on looplets, we introduce a …
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Automated Finetuning via Sparse Autoencoders
… a novel method using interpretability in sparse autoencoders to achieve better performance in small models via instruction finetuning. Specifically, we present UnderstandTune, an autonomous method for assembling high-quality instruction finetuning datasets with minimal human intervention, …
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