Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.

Abstract: 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.