Results 41 to 50 of about 201,485 (174)
Unsupervised Learning of Shape Manifolds [PDF]
Procedings of the British Machine Vision Conference 2007, 2007 Classical shape analysis methods use principal component analysis to reduce the dimensionality of shape spaces. The basic assumption behind these methods is that the subspace corresponding to the major modes of variation for a particular class of shapes is linearised. This may not necessarily be the case in practice.
Rajpoot, Nasir M. (Nasir Mahmood) +2 more
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Learning Parameterized Skills [PDF]
, 2012 We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies ...
Barto, Andrew +2 more
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Causal Learning via Manifold Regularization
Journal of machine learning research : JMLR, 2016 This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as `labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some
Hill, Steven M +3 more
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Manifold Learning and Nonlinear Homogenization
Multiscale Modeling & Simulation, 2022 We describe an efficient domain decomposition-based framework for nonlinear multiscale PDE problems. The framework is inspired by manifold learning techniques and exploits the tangent spaces spanned by the nearest neighbors to compress local solution manifolds.
Shi Chen +3 more
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Geodesic Distance Function Learning via Heat Flow on Vector Fields [PDF]
, 2014 Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such
He, Xiaofei +3 more
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Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
, 2018 The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years.
Ornek, Cem, Vural, Elif
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Visualizing Energy Landscapes through Manifold Learning
Physical Review X, 2021 Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration.
Benjamin W. B. Shires +1 more
doaj +1 more source
Learning on dynamic statistical manifolds
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020 Hyperbolic balance laws with uncertain (random) parameters and inputs are ubiquitous in science and engineering. Quantification of uncertainty in predictions derived from such laws, and reduction of predictive uncertainty via data assimilation, remain an open challenge.
F. Boso, D. M. Tartakovsky
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Manifold Approximation by Moving Least-Squares Projection (MMLS)
, 2019 In order to avoid the curse of dimensionality, frequently encountered in Big Data analysis, there was a vast development in the field of linear and nonlinear dimension reduction techniques in recent years.
Levin, David, Sober, Barak
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Error Metrics for Learning Reliable Manifolds from Streaming Data
, 2017 Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive.
Chandola, Varun +4 more
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