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Probabilistic and unsupervised machine learning for auditory data and pattern recognition

机译:用于听觉数据和模式识别的概率和无监督机器学习

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I will introduce and discuss probabilistic data models and their applications to auditory data and to general pattern recognition tasks. The introduction will reflect the view that powerful data models are able to extract the true compositional nature of data, which allows for a decomposition into their structural primitives. Probabilistic sparse coding and probabilistic version of non-negative matrix factorization (NMF) will be the first concrete models that are introduced and discussed. The state-of-the-art of these models will then be used to point to recent generalization directions. Two of these, translation invariant versions and deep generalizations, will be discussed in more detail. I will work out the benefits and challenges such novel approaches face, and I will discuss their crucial differences compared to supervised deep neural networks. Finally, I briefly discuss semi-supervised approaches, a field where modern unsupervised and modern supervised Machine Learning algorithms come together, compete and where they are combined.
机译:我将介绍和讨论概率数据模型及其在听觉数据和一般模式识别任务中的应用。简介将反映一种观点,即强大的数据模型能够提取数据的真实组成性质,从而可以分解成其结构基元。概率稀疏编码和非负矩阵分解(NMF)的概率版本将是引入和讨论的第一个具体模型。这些模型的最新技术将用于指出最新的推广方向。将更详细地讨论其中的两个,翻译不变式和深度概括。我将计算出这种新颖方法所面临的好处和挑战,并且将讨论它们与有监督的深度神经网络相比的关键区别。最后,我简要地讨论了半监督方法,在这个领域中,现代无监督和现代有监督的机器学习算法融合在一起,相互竞争,并在其中结合在一起。

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