Subject
Creator
- Bartlomiej P. Rajwa 2
- Murat Dundar 2
- Nicholas G. Smith, 0000-0001-7048-4387 2
- Baichuan Zhang 1
- Ferit Akova 1
- Halid Ziya Yerebakan 1
- Jeffrey Dukes 1
- Jeffrey Dukes, 0000-0001-9482-7743 1
- Michael Schuster 1
- Nicholas G Smith, 0000-0001-7048-4387 1
Type
- Dataset 5
Date
Total is 5 Results
Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
PRICLE community composition and soil moisture 2012-2013
10.4231/R7TQ5ZG3
Jeffrey Dukes
,
Michael Schuster
,
Nicholas G. Smith
12/01/2015
Plant community and soil moisture data at the Prairie Invasion and Climate Experiment (PRICLE) in 2012 and 2013
Agriculture Botany Climate environmental manipulation Forestry and Natural Resources Life Sciences plant community Plant Diversity Soil Water Content soils
BACE gas exchange 2011
10.4231/R7PC30B8
09/14/2016
BACE gas exchange data for 2011 season
Boston Area Climate Experiment Climate Change Forestry and Natural Resources Life Sciences photosynthesis respiration Vcmax
BACE gas exchange 2011
10.4231/R7959FJ4
Jeffrey Dukes
,
Nicholas G Smith
09/26/2016
BACE gas exchange data for 2011 season
Boston Area Climate Experiment Climate Change Forestry and Natural Resources Life Sciences photosynthesis respiration Vcmax
A Non-parametric Bayesian Model for Joint Cell Clustering and Cluster Matching: Identification of Anomalous Sample Phenotypes with Random Effects.
10.4231/R7KK98PG
Bartlomiej P. Rajwa , Ferit Akova , Halid Ziya Yerebakan , Murat Dundar
09/03/2014
The manuscript presents a non-parametric Bayesian algorithm called ASPIRE (Anomalous Sample Phenotype Identification with Random Effects) able to identify phenotypic differences across batches of cytometry samples in the presence of random effects
AML Bayesian Biomedical Engineering BMC Bioinformatics Computer Science cytometry Dirichlet process Gaussian mixture model Interdisciplinary Research Life Sciences random effects
Display #
Results 1 - 5 of 5