Abstract: This application relates to employing trained machine learning processes to predict demands of items during future temporal periods, and to determining a recommended price for the items. For example, a computing device may obtain sales data for an item, and may generate features based on the obtained sales data. The computing device may input the generated features to a trained machine learning process to generate output data characterizing a predicted demand of the item during a future temporal interval. Further, the computing device may determine a recommended price for the item based on the predicted demand and a budget allocation that corresponds to the item. The computing device may store the predicted demand of the item and the recommended price for the item in a data repository.

Abstract: System and method for transformer-based adversarial active learning system. A machine learning system includes a generator, a transformer encoder, a classifier, and a discriminator all working in combination to generate and select unlabeled data points for labeling. The system utilizes a generative adversarial network paired with an active learning framework to optimize text embedding and feature encoding according to distribution of training data.

Abstract: This application relates to employing trained machine learning processes to predict demands of items during future temporal periods, and to determining a recommended price for the items. For example, a computing device may obtain sales data for an item, and may generate features based on the obtained sales data. The computing device may input the generated features to a trained machine learning process to generate output data characterizing a predicted demand of the item during a future temporal interval. Further, the computing device may determine a recommended price for the item based on the predicted demand and a budget allocation that corresponds to the item. The computing device may store the predicted demand of the item and the recommended price for the item in a data repository.

Abstract: This application relates to employing trained machine learning processes to map items across various domains. For example, a computing device may obtain first textual data for a first item, and second textual data for a second item. The computing device generates a plurality of features based on the first textual data and the second textual data. Further, the computing device inputs the generated plurality of features to a trained machine learning process to generate output data characterizing a textual similarity between the first textual data and the second textual data. The computing device may also determine whether the first item maps to the second item based on the output data. The computing device generates mapping data based on the determination, and stores the mapping data in a data repository.