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Full-Text Articles in Physical Sciences and Mathematics
Understanding The Dynamic Visual World: From Motion To Semantics, Huaizu Jiang
Understanding The Dynamic Visual World: From Motion To Semantics, Huaizu Jiang
Doctoral Dissertations
We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in …
Graph Construction For Manifold Discovery, Cj Carey
Graph Construction For Manifold Discovery, Cj Carey
Doctoral Dissertations
Manifold learning is a class of machine learning methods that exploits the observation that high-dimensional data tend to lie on a smooth lower-dimensional manifold. Manifold discovery is the essential first component of manifold learning methods, in which the manifold structure is inferred from available data. This task is typically posed as a graph construction problem: selecting a set of vertices and edges that most closely approximates the true underlying manifold. The quality of this learned graph is critical to the overall accuracy of the manifold learning method. Thus, it is essential to develop accurate, efficient, and reliable algorithms for constructing …
Unsupervised Joint Alignment, Clustering And Feature Learning, Mohamed Marwan Mattar
Unsupervised Joint Alignment, Clustering And Feature Learning, Mohamed Marwan Mattar
Doctoral Dissertations
Joint alignment is the process of transforming instances in a data set to make them more similar based on a pre-defined measure of joint similarity. This process has great utility and applicability in many scientific disciplines including radiology, psychology, linguistics, vision, and biology. Most alignment algorithms suffer from two shortcomings. First, they typically fail when presented with complex data sets arising from multiple modalities such as a data set of normal and abnormal heart signals. Second, they require hand-picking appropriate feature representations for each data set, which may be time-consuming and ineffective, or outside the domain of expertise for practitioners. …