Pinned Repositories
2022_Explaining_Heterogeneity_in_Crop_Yield_Prediction_using_Remote_and_Proximal_Sensing
Dagstuhl
Drug_Analysis
dynamic-training-with-apache-mxnet-on-aws
Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.
joint-ES-estimate
This repository offers the R script used in Rillig et al. (20XX) to estimate joint effect sizes using bootstrap resampling, to estimate p-values, and to visualize them.
Taxonomic_Rank_Classification_of_Fungi_using_CNN_Image_Classifiers
Master Thesis
Taxonomy_Analysis__Fungi_ZooScan
Taxonomic Rank Classification with single- and multilabel CNN, pretrained DenseNet, Apache MXNET Framework
stillsen's Repositories
stillsen/2022_Explaining_Heterogeneity_in_Crop_Yield_Prediction_using_Remote_and_Proximal_Sensing
stillsen/Drug_Analysis
stillsen/Dagstuhl
stillsen/dynamic-training-with-apache-mxnet-on-aws
Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.
stillsen/joint-ES-estimate
This repository offers the R script used in Rillig et al. (20XX) to estimate joint effect sizes using bootstrap resampling, to estimate p-values, and to visualize them.
stillsen/Taxonomic_Rank_Classification_of_Fungi_using_CNN_Image_Classifiers
Master Thesis
stillsen/Taxonomy_Analysis__Fungi_ZooScan
Taxonomic Rank Classification with single- and multilabel CNN, pretrained DenseNet, Apache MXNET Framework