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 8 of 8 for “"Efficient ML"”.
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Efficient ML Inference via Matrix-Vector Approximations
Efficient inference is a growing priority in deep learning, where large model sizes and increasing deployment demands pose challenges for latency, memory, and energy usage. This thesis presents a unified framework for evaluating approximation methods that accelerate inference by modifying weight …
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Enabling Efficient ML Inference in SigmaOS with Model-Aware Scheduling
… and scheduling inefficiencies of multi-tenant ML serving by integrating the RayServe distributed model-serving framework into σOS, a cloud operating system that unifies container and serverless paradigms. The thesis also proposes two model-aware schedulers within σOS that intelligently routes …
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Structure-utilized, Adaptive, and Efficient ML-based Proportional-Fair Scheduling in MIMO Networks for Non-stationary Channels
… performance. More recently, machine learning (ML)-based approaches have demonstrated strong performance with low latency. However, ML-based methods typically assume stationary channel distributions, making them vulnerable to performance degradation under dynamic network conditions such as user …
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Ghostdecoding: leveraging random-feature kernels for error-aware and training-free KV cache selection
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation
Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not …
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Efficient, robust and uncertainty aware mobile health
… address the challenges above by developing new efficient-by-design frameworks for robust and uncertainty aware mobile health. Firstly, we introduce a framework that enables already trained deep learning models to generate uncertainty estimates on edge computing platforms with no need for …
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Efficient Distributed and Multi-Modal Machine Learning in Wireless Networks
… in that they will embed machine learning (ML) and AI techniques from the application layer down to the physical layer. However, training and deploying ML models in wireless networks presents two key challenges pertaining to the limited computing and resources of wireless devices and …
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Statistical error compensation for robust digital signal processing and machine learning
Machine learning (ML) based inference has recently gained importance as a key kernel in processing massive data in digital signal processing (DSP) systems. Due to the ever increasing complexity of DSP systems, energy-efficient ML accelerators are critical. Traditionally, energy efficiency was …