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 31 for “"Efficient Machine Learning"”.
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Efficient machine learning: models and accelerations
… enablers of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the …
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Data-Efficient Machine Learning for Computational Imaging
… prior knowledge from physical models into machine learning algorithms. Our approach optimizes image reconstruction from sparse and noisy datasets by utilizing physical constraints to guide deep learning models. This integration accelerates the imaging workflow, minimizes the need for large …
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Towards Workload-aware Efficient Machine Learning Systems
Machine learning (ML) is transforming various aspects of our lives, driving the need for computing systems that efficiently support large-scale ML workloads. As models grow in size and complexity, existing systems struggle to adapt, limiting both performance and flexibility. Additionally, ML …
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Reliable and efficient machine learning under distribution shifts
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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Data-Efficient Machine Learning with Applications to Cardiology
Deep learning models have demonstrated impressive capabilities in many settings including computer vision, natural language generation, and speech processing. However, an important shortcoming of these models is that they often need to be trained on large datasets in order to be most effective. In …
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Efficient Machine Learning with High Order and Combinatorial Structures
… frameworks, the inference algorithms, and the learning methods necessary for the accurate modeling of domains that exhibit complex and non-local dependency structures. There are three parts to this thesis. In the first part, we develop a toolbox of high order potentials (HOPs) that are useful …
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Data-Efficient Machine Learning with Focus on Transfer Learning
<p>Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms …
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Power efficient machine learning-based hardware architectures for biomedical applications
… and lightweight, requiring low-power, energy-efficient hardware platforms. Various machine learning models, such as deep learning architectures, have been employed to design intelligent healthcare systems. However, deploying these sophisticated and intelligent devices in real-time embedded …
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Data-efficient machine learning for decision-making in smart manufacturing
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Data-efficient machine learning for decision-making in smart manufacturing
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
Machine learning problems are increasing in complexity, so models are growing correspondingly larger to handle these datasets. (e.g., large-scale transformer networks for language modeling). The increase in the number of input features, model size, and output classification space is straining our …
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Efficient machine learning-based modeling for regional reliability analysis of infrastructure systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures
<p>Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load …
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Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. [Thesis]
… robust and effective detection of attacks. Machine learning (ML) and its subdivision Deep Learning (DL) methods offer a promise, but they can be computationally expensive in providing better detection for resource-constrained IoT devices. Therefore, this research proposes an optimization …
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TOWARDS DATA-EFFICIENT DEEP LEARNING
This thesis advances data-efficient machine learning by tackling the limitations of current dataset distillation (DD) methods, which aim to compress large datasets into compact synthetic ones for faster training and enhanced privacy. First, it introduces Dataset Factorization, a novel framework …
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Hierarchical Classification of Variable Stars Using Neural Networks
… astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can …
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Geometric optimization algorithms for linear regression on fixed-rank matrices
… encountered in many modern applications. Efficiently mining and exploiting these data sets generally results in the extraction of valuable information and therefore appears as an important challenge in various domains including network security, computer vision, internet search engines, …
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Optimized Calibration for Analog Computations Targeting Deep Neural Networks on the Example of BrainScaleS-2
Machine learning is pervasive today, but as more complex models are developed, their application is becoming increasingly costly. This work explores analog computing as a scalable and energy-efficient alternative to the typically used digital computations. Leveraging the BrainScaleS-2 system as an …
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Spiking Neural Networks for Low-Power Medical Applications
… However, the primary weakness of traditional machine learning for many applications is energy efficiency, and this may hamper its ability to be effectively utilized in medicine for portable or edge systems. In order to be more effective, new energy-efficient machine learning paradigms must be …
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Study of Thermoelectric and Lattice Dynamics Properties of 2D Layered MX (M = Sn, Pb; X = S, Se, Te) and ZrS2 Compounds using First-principles Approach
… is attributed to the increase of the Seebeck coefficient as a result of higher electronic density of states near the Fermi level in low-dimensional materials. In addition, lowering the dimensionality increases phonon scattering near interfaces and surfaces in 2D materials, which leads to a …
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