All the material needed to use MC-CP and the Adaptive MC Dropout method
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Updated
Sep 22, 2024 - Jupyter Notebook
All the material needed to use MC-CP and the Adaptive MC Dropout method
🤔 Methods for measuring and visualising the uncertainty in neural networks
Epistemic uncertainty, sometimes referred to as model uncertainty, describes what the model does not know because training data was not appropriate. Modelling epistemic uncertainty is crucial to prevent ill advised discussion making due to over confident models.
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)
Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods
Comparison of a network implemented via Variational Inference with the same network implemented via Monte Carlo Dropout
A Deep Learning Neural Network that classifies Vector Like Quarks from background events using generated collider data
An experimental Python package for learning Bayesian Neural Network.
Bayesian deep learning experiments
(Forked Version) Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
Probabilistic approach to neural nets - modern scalable approximate inference methods
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
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