A Simple Baseline for Bayesian Uncertainty in Deep Learning

TensorFlow implementation of “A Simple Baseline for Bayesian Uncertainty in Deep Learning”

Concept

Algorithm to utilize the SWAG [1].

Equation for the weight sampling from SWAG [1].

Results

The red color and the blue color represent the initial state and current state respectively.

Performance

MNIST

Method	Accuracy	Precision	Recall	F1-Score
Final Epoch	0.99230	0.99231	0.99222	0.99226
Best Loss	0.99350	0.99350	0.99338	0.99344
SWAG (S = 30)	0.99310	0.99305	To finish reading, please visit source site Categories Categories Search for: Recent Posts Quiz: Python MarkItDown: Convert Documents Into LLM-Ready Markdown Python MarkItDown: Convert Documents Into LLM-Ready Markdown Magentic Marketplace: an open-source simulation environment for studying agentic markets Building UIs in the Terminal With Python Textual RedCodeAgent: Automatic red-teaming agent against diverse code agents Tags Attention blogathon Calculus Command-line Tools Data Preparation data science data visualization Deep Learning Deep Learning for Computer Vision Deep Learning for Natural Language Processing Deep Learning for Time Series Deep Learning Performance Deep Learning with PyTorch Ensemble Learning Generative Adversarial Networks Imbalanced Classification Linear Algebra Long Short-Term Memory Networks machine learning Machine Learning Algorithms Machine Learning Process Machine Learning Resources machine translation Matplotlib Natural language processing Natural Language Processing & Speech Neural MT nlp NMT opencv Optimization pandas Probability python Python for Machine Learning Python Machine Learning Resources R Machine Learning scikit-learn sentiment analysis Start Machine Learning Statistics Time Series Weka Machine Learning XGBoost Categories Categories Archives Archives Powered by WordPress and Rubine.

Method

Accuracy

Precision

Recall

F1-Score

Final Epoch

0.99230

0.99231

0.99222

0.99226

Best Loss

0.99350

0.99338

0.99344

SWAG (S = 30)

0.99310

0.99305

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