uncertainty-sampling

There are 2 repositories under uncertainty-sampling topic.

ntucllab / libact
Pool-based active learning in Python
machine-learning-library active-learning machine-learning uncertainty-sampling
Language:Python 777
SURGroup / UQpy
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
uncertainty-quantification uncertainty-propagation uncertainty-sampling probability probabilistic stochastic stochastic-process monte-carlo monte-carlo-simulation latin-hypercube latin-hypercube-sampling
Language:Python 271
MonteCarloMeasurements.jl
baggepinnen / MonteCarloMeasurements.jl
Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
monte-carlo monte-carlo-sampling numeric-types uncertainty-propagation uncertainty-sampling error-propagation uncertainties physical-quantities error-analysis robust-optimization probability-distributions particle-filter monte-carlo-simulation gpu-acceleration gpu-computing
Language:Julia 258
dsgissin / DiscriminativeActiveLearning
Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For the blog:
active-learning deep-learning uncertainty-sampling core-set adversarial-active-learning bayesian-active-learning egl machine-learning
Language:Python 200
ahhaque / ECHO
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
datastream classification semi-supervised uncertainty-sampling dynamic-programming classifier-confidence
Language:Java 11
ahhaque / SAND
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
datastream classification semi-supervised uncertainty-sampling classifier-confidence
Language:Java 11
ryanquinnnelson / CMU-02750-Query-Selection-Methods-in-Active-Learning
Spring 2021 - Automation of Scientific Research - course project
active-learning algorithm-implementation type-1-active-learning iwal modal-library random-forest-classifier logistic-regression uncertainty-sampling density-based-sampling query-by-committee max-disagreement-sampling
Language:Jupyter Notebook 4
sid-devic / OpAL
Open Active Learning for Deep Models
deep-models uncertainty-sampling query-strategy opal
Language:Python 1
tupui / PHD-Thesis
PHD Thesis at CERFACS: Uncertainty Quantification for High Dimensional Problems
sensitivity-analysis uncertainty-quantification uncertainty-visualisation uncertainty-sampling computational-fluid-dynamics
Language:TeX 1
eurobios-mews-labs / active-bagging-learning
This library proposes a plug-in approach to active learning utilizing bagging techniques. Bagging, or bootstrap aggregating, is an ensemble learning method designed to improve the stability and accuracy of machine learning algorithms.
active-learning bagging machine-learning ml surrogate-models latin-square uncertainty-sampling
Language:Python

uncertainty-sampling

ntucllab / libact

SURGroup / UQpy

baggepinnen / MonteCarloMeasurements.jl

dsgissin / DiscriminativeActiveLearning

ahhaque / ECHO

ahhaque / SAND

ryanquinnnelson / CMU-02750-Query-Selection-Methods-in-Active-Learning

sid-devic / OpAL

tupui / PHD-Thesis

eurobios-mews-labs / active-bagging-learning