Romen Samuel Rodis Wabina's repositories
SDENet-UQ-ESL
SDENet as Uncertainty Quantification Method for EEG Source Localization
ConductorNetwork
Implementation of Conductor Network for deep learning-based conductivity estimation
RADI605
The in-depth coverage of machine learning algorithms; statistical objectives of the prediction; classification and clustering; data validation; cluster analysis; establishing predictive and classification models; using machine learning applications in the health analytics; python programming for modern machine learning.
CNN-Nodule-Classification
In this repository, we utilized Convolutional Neural Networks (CNN) to develop a binary classification model in detecting nodules in CT scans.
Computational-Statistics
This is the repository for the Computational Statistics course covering numerical linear algebra, Gaussian processes, Newton’s method and optimization, numerical integration, Markov chain Monte Carlo (MCMC), the Bootstrap, density estimation, and machine learning (neural networks and deep learning).
NLP-Medical-Specialty-Classification
In this repository, we utilized BERT-based approaches to classify medical specialities using transcription data.
TableOCR
TableNet Implementation via PyTorch
UnderstandingDNA
Explore the applications of Natural Language Understanding in Bioinformatics, focusing on the Central Dogma of Molecular Biology,
ChromeHttpRequestBlocker
Chrome extension that allows blocking HTTP request based on URL pattern definitions.
easylist
EasyList filter subscription (EasyList, EasyPrivacy, EasyList Cookie, Fanboy's Social/Annoyances/Notifications Blocking List)
Open-Cookie-Database
The Open Cookie Database is an effort to describe and categorise all major cookies. All cookie descriptions are saved in a downloadable CSV file. All contributions to the CSV file are welcomed.
PhD-Literature-Review
This repository summarizes my literature review across different fields of Data Science, Artificial Intelligence, and Informatics and its application to medicine and public health.
SDE-Net
Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
uncertainty
Here's a simple tutorial on uncertainty quantification using Monte Carlo (MC) dropout. The dropout, originally used to avoid over fitting, can provide model uncertainty. This repository will be updated with new UQ models
uq-course
Introduction to Uncertainty Quantification
VISSIM-Calibration-via-LHD-GA
Latin Hypercube Design + Genetic Algorithm