Improvement of qBayesianActiveLearningByDisagreement #2457
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Codecov ReportAll modified and coverable lines are covered by tests ✅
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## main #2457 +/- ##
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Coverage 99.98% 99.98%
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Files 191 191
Lines 16789 16795 +6
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+ Hits 16787 16793 +6
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Reviewed By: saitcakmak Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Reviewed By: saitcakmak Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Reviewed By: saitcakmak Differential Revision: D60308502
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Summary: Pull Request resolved: pytorch#2457 Improvement of the implementation of qBayesianActiveLearningByDisagreement - Utilizes a Monte Carlo approach for approximating the entropy - Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions - Can accept posterior transforms - get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers Reviewed By: saitcakmak Differential Revision: D60308502
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Summary:
Improvement of the implementation of qBayesianActiveLearningByDisagreement
Utilizes a Monte Carlo approach for approximating the entropy
Does not use concatenate_pending_points, as it is not evident that fantasizing makes sense in the same way as for standard MC acquisition functions
Can accept posterior transforms
get_model and get_fully_bayesian_model are used in tests to be similar to other tests (e.g. JES & the subsequent active learning acqfs to enable move to test_helpers
Differential Revision: D60308502