CONFIDENCE EVALUATION MODEL FOR STRUCTURE PREDICTION TASKS

Techniques for training for and determining a confidence of an output of a machine learning model are disclosed. Such techniques include, in some embodiments, receiving, from the machine learning model configured to receive information associated with a data object, information associated with a pre...

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Main Authors Tensmeyer, Christopher, Madapoosi Ravi, Sruthi, Mathur, Priyank, Barmpalios, Nikolaos, Deshpande, Ruchi, Bangalore Naresh, Smitha, Sido, Oghenetegiri, Manjunatha, Varun
Format Patent
LanguageEnglish
Published 25.01.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Techniques for training for and determining a confidence of an output of a machine learning model are disclosed. Such techniques include, in some embodiments, receiving, from the machine learning model configured to receive information associated with a data object, information associated with a predicted structure for the data object; encoding, using a second machine learning model, the information associated with the predicted structure for the data object to produce encoded input channels; evaluating, using the second machine learning model, the information associated with the data object with the encoded input channels; and based on the evaluating, determining, using the second machine learning model, a probability of correctness of the predicted structure for the data object.
Bibliography:Application Number: US202217815448