Results 241 to 250 of about 6,366,089 (278)
Advanced Science, EarlyView.A soft hybrid multi‐wavelength PPG wearable acquires neonatal signals. Synchronized PPG and invasive ABP data are segmented into fixed windows. A 1D‐EfficientNet model predicts segment‐level SBP and DBP. Model performance is examined with retrospective subgroup analysis across acquisition conditions.
Wenqi Shi +12 more
wiley +1 more source
Data-driven prediction of α<sub>IIb</sub>β<sub>3</sub> integrin activation paths using manifold learning and deep generative modeling. [PDF]
Biophys JDasetty S, Bidone TC, Ferguson AL.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Hongbin Zha
exaly +4 more sources
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Hongbin Zha
exaly +4 more sources
MANIFOLD LEARNING FOR ROBOT NAVIGATION
International Journal of Neural Systems, 2006 In this paper we introduce methods to build a SOM that can be used as an isometric map for mobile robots. That is, given a dataset of sensor readings collected at points uniformly distributed with respect to the ground, we wish to build a SOM whose neurons (prototype vectors in sensor space) correspond to points uniformly distributed on the ground ...
Keeratipranon, Narongdech +2 more
openaire +5 more sources
Enhance explainability of manifold learning
Neurocomputing, 2022Henry Han, Wentian Li
exaly +3 more sources
IEEE Signal Processing Magazine, 2011
We present algorithms for analyzing massive and high-dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated.
Jamshidi, Arta +2 more
openaire +3 more sources
We present algorithms for analyzing massive and high-dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated.
Jamshidi, Arta +2 more
openaire +3 more sources
Neurocomputing, 2014
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li +2 more
openaire +1 more source
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li +2 more
openaire +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang 0001, Sridhar Mahadevan
openaire +1 more source
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang 0001, Sridhar Mahadevan
openaire +1 more source