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 “"Machine Learning Benchmarks"”.
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Applications of Computational Geometry and Computer Vision
Recent advances in machine learning research promise to bring us closer to the original goals of artificial intelligence. Spurred by recent innovations in low-cost, specialized hardware and incremental refinements in machine learning algorithms, machine learning is revolutionizing entire …
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Essays on the Decision Value of Data in Marketing Measurement and Targeting
… value achieved by non-privacy-preserving machine learning benchmarks. Together, the three essays characterize when data is helpful for personalization and marketing measurement, when it is not, and how data collection can be redesigned to improve its decision value.
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Adapting deep neural networks as models of human visual perception
… perception: (1) deep representational distance learning (RDL), a method for driving representational spaces in deep nets into alignment with other (e.g. brain) representational spaces and (2) variational DNNs that use sampling to perform approximate Bayesian inference. In the first …
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Sculpting representations for deep learning
In machine learning, the choice of space in which to represent our data is of vital importance to their effective and efficient analysis. In this thesis, we develop approaches to address a number of problems in representation learning. We employ deep learning as means of sculpting our …
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Decision Making for Populations
… large heterogeneous populations by privately learning from decentralized data sources. First, we introduce DeepABM a framework for Scalable, Fast and Differentiable Agent-based Modeling. DeepABM can simulate million-size populations in a few seconds on personal computers (up to 300x faster …
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A comparative study of recurrent neural networks and statistical techniques for forecasting the stock prices of JSE-listed securities
As machine learning has developed, the attention of stock price forecasters has slowly shifted from traditional statistical forecasting techniques towards machine learning techniques. This study investigated whether machine learning techniques, in particular, recurrent neural networks, do indeed …
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Towards externally valid machine learning: A spurious correlations perspective
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Toward effective and generalisable machine learning for biosignal time series
… thesis addresses these challenges by developing machine learning methods tailored for biosignal time series that focus on self-supervised representation learning, domain adaptation, and foundation modelling for generalisation across tasks and datasets. We demonstrate the efficacy of these methods …