GitHub topics: monte-carlo-dropout
Romilagarwal/diabetic_retinoplasty
A hybrid deep learning framework for automated diabetic retinopathy detection combining EfficientNetB0 with Swin Transformer attention mechanisms. Features Bayesian uncertainty quantification through Monte Carlo Dropout, explainable AI visualizations with Grad-CAM, and specialized preprocessing techniques.
Language: Python - Size: 52.4 MB - Last synced at: 13 days ago - Pushed at: 14 days ago - Stars: 0 - Forks: 0

realjules/monitor.ai
monitor.ai: Non-intrusive monitoring for FDA-approved medical AI, helping healthcare organizations ensure safety and compliance without modifying validated models.
Language: Jupyter Notebook - Size: 163 MB - Last synced at: 28 days ago - Pushed at: 28 days ago - Stars: 0 - Forks: 0

team-daniel/MC-CP
All the material needed to use MC-CP and the Adaptive MC Dropout method
Language: Jupyter Notebook - Size: 4.07 MB - Last synced at: 5 months ago - Pushed at: 7 months ago - Stars: 21 - Forks: 4

lorenzobandini/U-ProBE
Analyzing deep learning models with uncertainty in predictions
Language: Python - Size: 736 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

harrisonpim/uncertainty
🤔 Methods for measuring and visualising the uncertainty in neural networks
Language: Jupyter Notebook - Size: 168 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

FedericoVasile1/bayesian-cnn
Comparison of a network implemented via Variational Inference with the same network implemented via Monte Carlo Dropout
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JortdeJong13/epistemic-uncertainty
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.
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negarhdr/FER_PSTBLN_MCD
PyTorch implementation of landmark-based facial expression recognition using Spatio-Temporal BiLinear Networks (ST-BLN)
Language: Python - Size: 106 KB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 4 - Forks: 1

akashmondal1810/UncertaintyEstimation
Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods
Language: Python - Size: 9.29 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 15 - Forks: 5

ronaldseoh/bayesian-dl-experiments
Bayesian deep learning experiments
Language: Jupyter Notebook - Size: 171 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 8 - Forks: 2

sungyubkim/MCDO
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
Language: Jupyter Notebook - Size: 233 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 37 - Forks: 7

kenneym/bug_classification_research
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.
Language: Jupyter Notebook - Size: 41.1 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 4 - Forks: 1

ronaldseoh/ronald_bdl
An experimental Python package for learning Bayesian Neural Network.
Language: Python - Size: 60.5 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 6 - Forks: 1

lpcinelli/probabilistic-nn
Probabilistic approach to neural nets - modern scalable approximate inference methods
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ronaldseoh/DropoutUncertaintyExps Fork of yaringal/DropoutUncertaintyExps
(Forked Version) Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
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GilbertoCunha/LHC-VLQ-Classifier
A Deep Learning Neural Network that classifies Vector Like Quarks from background events using generated collider data
Language: Jupyter Notebook - Size: 9.18 MB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
