Results 51 to 60 of about 5,853,511 (292)
A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
Entropy, 2018 Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge.
Xulun Ye, Jieyu Zhao, Yu Chen
doaj +1 more source
Learning Generative Models across Incomparable Spaces
, 2019 Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety.
Alvarez-Melis, David +3 more
core +1 more source
Manifold Learning by Graduated Optimization [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011 We present an algorithm for manifold learning called manifold sculpting , which utilizes graduated optimization to seek an accurate manifold embedding. An empirical analysis across a wide range of manifold problems indicates that manifold sculpting yields more accurate results than a number of existing algorithms, including Isomap, locally linear ...
M, Gashler, D, Ventura, T, Martinez
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Manifold for machine learning assurance [PDF]
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results, 2020 The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system ...
Byun, Taejoon, Rayadurgam, Sanjai
openaire +2 more sources
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
core +2 more sources
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
core +1 more source
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
Parametric Local Metric Learning for Nearest Neighbor Classification [PDF]
, 2012 We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics.
Kalousis, Alexandros +2 more
core
Locality Preserving Projections for Grassmann manifold
, 2017 Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.
Chen, Haoran +5 more
core +1 more source
Nature Communications, 2020 Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development.
Guang Yang +8 more
semanticscholar +1 more source