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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 14 of 14 for “"large scale machine learning"”.
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Large Scale Machine Learning in Biology
… could lead to deeper biological understanding. Large volumes of data provided by such technologies, however, are not analyzable using hypothesis-driven significance tests and other cornerstones of orthodox statistics. We present powerful tools in machine learning and statistical inference for …
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Large-Scale Machine Learning for Classification and Search
… or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach …
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Multi-objective resource optimization for large scale machine learning systems
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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Metagradient Descent: Differentiating Large-Scale Training
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for …
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Towards Understanding Privacy Leakage in Decentralized and Collaborative Learning
The emergence of large-scale machine learning (ML) models has highlighted a fundamental conflict: While computational demands push for the consolidation of data and models in vast, centralized data centers, real-world data continues to be distributed and fragmented across personal devices and …
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BeatDB : an end-to-end approach to unveil saliencies from massive signal data sets
… from 6 to 12 months. In response we design a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining knowledge from waveforms. BeatDB radically shrinks the time an investigation takes by: * supporting fast, flexible investigations by offering a multi-level …
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PhysioMiner : a scalable cloud based framework for physiological waveform mining
This work presents PhysioMiner, a large scale machine learning and analytics framework for physiological waveform mining. It is a scalable and flexible solution for researchers and practitioners to build predictive models from physiological time series data. It allows users to specify arbitrary …
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Large scale optimization for machine learning
… decades, tremendous tools have been developed in machine learning, ranging from statistical models to scalable algorithms, from learning strategies to various tasks, having a far-reaching influence in broad applications ranging from image and speech recogni- tions to recommender systems, and from …
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New directions in streaming algorithms
Large volumes of available data have led to the emergence of new computational models for data analysis. One such model is captured by the notion of streaming algorithms: given a sequence of N items, the goal is to compute the value of a given function of the input items by a small number of passes …
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Robot learning with strong priors
Embedding learning ability in robotic systems is one of the long sought-after objectives of artificial intelligence research. Despite the recent advancements in hardware, large-scale machine learning algorithms and theoretical understanding of deep learning, it is still quite unrealistic to deploy …
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Everything old is new again : a fresh look at historical approaches in machine learning
… shows that several old, somewhat discredited machine learning techniques are still valuable in the solution of modern, large-scale machine learning problems. We begin by considering Tikhonov regularization, a broad framework of schemes for binary classification. Tikhonov regularization …
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Optimization Methods for Machine Learning under Structural Constraints
In modern statistical and machine learning models, structural constraints are usually imposed for model interpretability as well as model complexity reduction. In this thesis, we present scalable optimization methods for several large-scale machine learning problems under structural constraints, …
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Communication efficient large scale distributed optimization with curvature acceleration
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
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Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications
… surveillance sensor systems, and so on. On these large scale data sets, nearest neighbor search is fundamental for lots of applications including content based search/retrieval, recommendation, clustering, graph and social network research, as well as many other machine learning and data mining …