Results 231 to 240 of about 6,131,436 (285)

Unsupervised learning for real-time and continuous gait phase detection. [PDF]

PLoS One
Anopas D, Wongsawat Y, Arnin J.
europepmc   +1 more source

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach. [PDF]

Sci Rep
Raju ASN, Venkatesh K, Gatla RK, Konakalla EP, Eid MM, Titova N, Ghoneim SSM, Ghaly RNR.   +7 more
europepmc   +1 more source

An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images. [PDF]

Sensors (Basel)
Fraschini M, Castagnola M, Barberini L, Sanfilippo R, Coghe F, Didaci L, Cau R, Frongia C, Scartozzi M, Saba L, Faa G.   +10 more
europepmc   +1 more source

ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning.

Digit Discov
Pinheiro M, de Oliveira Bispo M, Mattos RS, Telles do Casal M, Chandra Garain B, Toldo JM, Mukherjee S, Barbatti M.   +7 more
europepmc   +1 more source

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Unsupervised learning

American Journal of Orthodontics and Dentofacial Orthopedics, 2023
This work was supported by the Flemish Government under the “Onderzoeksprogramma Artifici€ele Intelligentie (AI) Vlaanderen ...
Dirk Valkenborg, Axel-Jan Rousseau, Melvin Geubbelmans, Tomasz Burzykowski   +3 more
  +11 more sources

Unsupervised Learning

ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2017
Laura Igual, Santi Seguí
semanticscholar   +6 more sources

Unsupervised Learning Methods for Molecular Simulation Data

Chemical Reviews, 2021
Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry.
Aldo Glielmo, Brooke E Husic, Alex Rodriguez   +2 more
exaly   +2 more sources

ScaleNet: An Unsupervised Representation Learning Method for Limited Information

German Conference on Pattern Recognition, 2023
Although large-scale labeled data are essential for deep convolutional neural networks (ConvNets) to learn high-level semantic visual representations, it is time-consuming and impractical to collect and annotate large-scale datasets.
Huili Huang, M. M. Roozbahani
semanticscholar   +1 more source

Transformer-based unsupervised contrastive learning for histopathological image classification

Medical Image Anal., 2022
A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large annotations is very challenging, especially for histopathological images with unique characteristics (e.g.,
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Junzhou Huang, Xiao Han   +7 more
semanticscholar   +1 more source