Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

Abstract: Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

Abstract: Embodiments relate to a system and a method for identifying, from contractual documents, (i) standard exact clauses matching clause examples and (ii) non-standard clauses semantically related to but not matching the clause examples. A standard feature data set comprising standard exact clauses matching clause examples is obtained. In addition, a mirror feature data set comprising semantically related clauses of the clause examples is obtained using semantic language analysis, where the mirror feature data set encompasses the standard feature data set. Non-standard clauses are obtained by extracting a difference between the mirror feature data set and the standard exact feature data set.