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 9 of 9 for “"Maximum mean discrepancy"”.
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ASSESSMENT OF AI-GENERATED IMAGES USING COMPUTATIONAL METRICS AND HUMAN CENTRIC ANALYSIS
… attention for local similarity and Maximum Mean Discrepancy (MMD) for global distributional similarity. Our evaluation showed that GLIPS aligns more closely with human perception compared to traditional metrics like FID and SSIM for photorealism, although MS-SSIM outperformed in …
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Distribution distance measures in generative and privacy models
… and increases the model's ability to generalize. Maximum mean discrepancy and energy distance are two such metrics that are easily defined and implemented over samples, and provide meaningful results on a range of data sets and data types. This work presents three main contributions: (1) a novel …
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Statistical inference for high-dimensional data
… include the well-known energy distance and maximum mean discrepancy with Gaussian and Laplacian kernels, and the critical values are obtained via permutations. We show that all these tests are inconsistent when the two high dimensional distributions correspond to the same marginal …
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Scalable Methodologies for Optimizing Over Probability Distributions
… of a family of popular interaction energies—maximum mean discrepancy of mean-zero kernels—to generate high-quality coresets from millions of biased samples, obtaining better-than-i.i.d. unbiased coresets. The second part transitions to optimizing over continuous distributions through neural …
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On Sequence Clustering and Supervised Dimensionality Reduction
… either the Kolmogrov-Smirnov distance or the maximum mean discrepancy is used as the distance metric. Tighter upper bound on the error probability of the single-linkage HAC algorithm is derived by taking advantage of the simplified metric updating scheme. Numerical results are provided to …
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Statistical Learning for Sequential Unstructured Data
… vectors. Subsequently, a kernel test using maximum mean discrepancy is employed to detect abnormal segments within a given time period. Theoretical results showed that learning from numerical vectors is equivalent to learning directly through the raw data. A real-world example illustrates …
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Investigating Topological Quantum Matter: Machine Learning Topological Phases, Topological Quantum Codes, Interplay of Disorder and Topology via Transport Phenomena and Phase Transitions
… and hybrid optimisation: Chapter 2 recasts maximum-likelihood decoding of CSS stabiliser codes as a weighted Max-3-SAT instance that can be solved in the clause-sparse, algorithmically easy regime, and Chapter 3 develops a depth-1 Born-machine circuit that minimises a …
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Nonparametric Anomaly Detection and Secure Communication
… tests are proposed, which are based on maximum mean discrepancy (MMD) that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. These tests are nonparametric without exploiting the information about $p$ and $q$ and are universally …
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On Classification in Human-driven and Data-driven Systems
… of error. Error exponent analysis using the maximum mean discrepancy is provided and the discrimination rate, i.e., lower bound on the discrimination capacity is characterized. Furthermore, an upper bound on the discrimination capacity based on Fano's inequality is developed.</p>