Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques
<p>Gaining insights into complex high-dimensional data is challenging and typically requires the use of dimensionality reduction methods. These methods let us identify low-dimensional structures embedded within the data that may reveal patterns of interest. In probabilistic models, such low-di...
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| 格式: | Thesis |
| 語言: | English |
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2019
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