Pinned Repositories
datascir21
Materials for the seminar "Data Science with R" held at the Otto von Guericke University Magdeburg
Deep-Embedded-Clustering
Deep-Learning
Machine-Learning-Engineering-for-Production-MLOps-
Machine_Learning_Implementation
oeplatform
Repository for the code of the Open Energy Platform (OEP) website. The OEP provides an interface to the Open Energy Family
omi
Repository for the Open Metadata Integration (OMI). For metadata definition see metadata repo:
Rscraping
SharanyaMohan-30
Config files for my GitHub profile.
Supporting-SLR-Using-DL-Based-Language-Models
The repository contains code scripts for replicating experiments in the paper "Supporting Systematic Literature Reviews Using Deep-Learning-Based Language Models". In this paper, we address the tedious process of identifying relevant primary studies during the conduct phase of a Systematic Literature Review. For this purpose, we use deep learning architectures in the form of the two language models BERT and S-BERT to learn embedded representations and cluster on them to semi-automate this phase, and thus support the entire SLR process.
SharanyaMohan-30's Repositories
SharanyaMohan-30/datascir21
Materials for the seminar "Data Science with R" held at the Otto von Guericke University Magdeburg
SharanyaMohan-30/Deep-Embedded-Clustering
SharanyaMohan-30/Deep-Learning
SharanyaMohan-30/Machine-Learning-Engineering-for-Production-MLOps-
SharanyaMohan-30/Machine_Learning_Implementation
SharanyaMohan-30/oeplatform
Repository for the code of the Open Energy Platform (OEP) website. The OEP provides an interface to the Open Energy Family
SharanyaMohan-30/omi
Repository for the Open Metadata Integration (OMI). For metadata definition see metadata repo:
SharanyaMohan-30/Rscraping
SharanyaMohan-30/SharanyaMohan-30
Config files for my GitHub profile.
SharanyaMohan-30/Supporting-SLR-Using-DL-Based-Language-Models
The repository contains code scripts for replicating experiments in the paper "Supporting Systematic Literature Reviews Using Deep-Learning-Based Language Models". In this paper, we address the tedious process of identifying relevant primary studies during the conduct phase of a Systematic Literature Review. For this purpose, we use deep learning architectures in the form of the two language models BERT and S-BERT to learn embedded representations and cluster on them to semi-automate this phase, and thus support the entire SLR process.
SharanyaMohan-30/wilds
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.