A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

All the material needed to use MC-CP and the Adaptive MC Dropout method

Hybrid CNN-Transformer model for automated exoplanet transit detection on NASA Kepler light curves. Features dual scale CNN branches, Transformer based sequence modelling, Grad CAM + SHAP + attention explainability, and Monte Carlo Dropout uncertainty quantification. 5 fold cross validated with baseline comparisons.

Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods

Bayesian deep learning experiments

PyTorch-native Bayesian neural networks for uncertainty quantification in deep learning

An experimental Python package for learning Bayesian Neural Network.

An NLP Model used for automated assignment of bug reports to the relevant engineering team. Utilizes a novel confidence bounding approach - Monte Carlo Dropout, and assigns underconfident predictions to a queue for human review. Built for Pegasystems Inc.

PyTorch implementation of landmark-based facial expression recognition using Spatio-Temporal BiLinear Networks (ST-BLN)

Hybrid Uncertainty Quantification for Bioactivity Assessment

Comparison of a network implemented via Variational Inference with the same network implemented via Monte Carlo Dropout

(Forked Version) Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

GUI for analyzing deep learning models with uncertainty in predictions

monitor.ai: Non-intrusive monitoring for FDA-approved medical AI, helping healthcare organizations ensure safety and compliance without modifying validated models.

Probabilistic approach to neural nets - modern scalable approximate inference methods

Waveform based ventricular tachycardia alarm validation using a 1D CNN + LSTM architecture with Monte Carlo Dropout for uncertainty estimation.

Loto6 lottery number prediction with BiLSTM + Monte Carlo Dropout (educational use)

RAG system with Bayesian uncertainty quantification using Beta priors and Monte Carlo Dropout

Uncertainty-aware financial forecasting for the Borsa Istanbul 100 Index. Transformer model with Monte Carlo Dropout for epistemic uncertainty, integrating Turkish equity, US equity, and FRED-MD macro data. Coursework for COM0415 Applied Deep Learning.