Abstract: Scannable thumbnails for identifying data objects stored on a database. A method includes identifying a data collection comprising one or more data objects stored on a database and receiving a thumbnail selection to represent the data collection, wherein the thumbnail selection comprises one or more of an image from the data collection, a graphic, or text. The method includes generating a scannable code associated with the data collection merging the scannable code with the thumbnail selection to generate a scannable thumbnail.

Abstract: Scannable thumbnails for identifying data objects stored on a database. A method includes identifying a data collection comprising one or more data objects stored on a database and receiving a thumbnail selection to represent the data collection, wherein the thumbnail selection comprises one or more of an image from the data collection, a graphic, or text. The method includes generating a scannable code associated with the data collection merging the scannable code with the thumbnail selection to generate a scannable thumbnail.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving a complete set of training data; receiving instructions to train a predictive model having a plurality of parameters on an initial subset of the complete set of training data; training the predictive model on the initial subset; storing data representing a first state of the predictive model after training the predictive model on the initial subset; receiving updated parameter values and instructions to train the predictive model on a new subset of the complete set of training data; and training the predictive model on the new subset.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes performing experiments to select a training strategy for use in training the model on a particular data set. The selected training strategy includes a binning strategy for binning the raw feature vectors before the raw feature vectors are provided to the predictive model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning models. The models can include models for predicting a next transaction price or a next transaction price direction for one or more financial products, for classifying particular debit or credit card transactions as likely being anomalous or fraudulent or not, or for classifying particular financial claims processing transactions, e.g., insurance, health care, or employee expense claims transactions, as likely being anomalous or fraudulent or not.