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

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

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)

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

Hybrid Uncertainty Quantification for Bioactivity Assessment

(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

Probabilistic approach to neural nets - modern scalable approximate inference methods

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

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

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)

11th place solution for the U-Tokyo Deep Learning Course MLP Competition (Top 0.8%). High-performance MLP implemented from scratch in NumPy, featuring AdamW, EMA, SWA, and MC Dropout.

Devetak, Davide and Scuor, Nicola, Bayesian Deep Learning for Commodity Price Forecasting: Applications to Steel Procurement and Hedging Strategies (August 20, 2025).