Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data ; volume:7 ; number:1 ; day:11 ; month:6 ; year:2024 ; pages:1-16 ; date:12.2024
Communications chemistry ; 7, Heft 1 (11.6.2024), 1-16, 12.2024
- Creator
-
Li, Tonglei
Huls, Nicholas J.
Lu, Shan
Hou, Peng
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s42004-024-01217-z
- URN
-
urn:nbn:de:101:1-2408191003595.147048621285
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:49 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Li, Tonglei
- Huls, Nicholas J.
- Lu, Shan
- Hou, Peng
- SpringerLink (Online service)
Other Objects (12)
Neural embedding: learning the embedding of the manifold of physics data
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Manifold learning and maximum likelihood estimation for hyperbolic network embedding
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Graph Embedding, Content Understanding, and Self-Supervised Learning
Unsupervised Learning of Network Embedding with Variational Autoencoder Framework
Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Unsupervised Ensemble Learning Improves Discriminability of Stochastic Neighbor Embedding
Neural embedding: learning the embedding of the manifold of physics data
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Manifold learning and maximum likelihood estimation for hyperbolic network embedding
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Graph Embedding, Content Understanding, and Self-Supervised Learning
Unsupervised Learning of Network Embedding with Variational Autoencoder Framework
Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Unsupervised Ensemble Learning Improves Discriminability of Stochastic Neighbor Embedding
Neural embedding: learning the embedding of the manifold of physics data
Towards more reliable machine learning: conceptual insights and practical approaches for unsupervised manifold learning and supervised benchmark studies
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Manifold learning and maximum likelihood estimation for hyperbolic network embedding
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Graph Embedding, Content Understanding, and Self-Supervised Learning
Unsupervised Learning of Network Embedding with Variational Autoencoder Framework
Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding