Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine learning. One of the methods includes receiving a document having a plurality of first text strings; extracting the plurality of first text strings from the document; providing the extracted plurality of first text strings to a first machine learning model, wherein the first machine learning model is trained to output a numerical vector representation for each input first text string; providing the output vector representations from the first machine learning model to a second machine learning model, wherein the second machine learning model is trained to output a second text string for each input vector representation; and processing the second text strings to generate an output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine learning. One of the methods includes receiving a document having a plurality of first text strings; extracting the plurality of first text strings from the document; providing the extracted plurality of first text strings to a first machine learning model, wherein the first machine learning model is trained to output a numerical vector representation for each input first text string; providing the output vector representations from the first machine learning model to a second machine learning model, wherein the second machine learning model is trained to output a second text string for each input vector representation; and processing the second text strings to generate an output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a plurality of data points associated with a parcel of real property; using a machine learning model to generate a prediction from the obtained plurality of data points, the prediction indicating a likelihood that the real property will satisfy a particular parameter, wherein the machine learning model is trained using a training set comprising a collection of data points associated with a labeled set of real property parcels distinct from the specified parcel of real property, the label indicating the particular parameter and corresponding value for each real property parcel of the training set; and based on the prediction, classifying the specified parcel of real property according to a determination of whether the predicted value of the parameter satisfies a threshold.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a collection of training data, the training data comprising collection of data points associated with a labeled set of real property parcels; training a machine learning model using the training data, the machine learning model being trained to generate a likelihood with respect to a parameter from input data associated with a specific parcel of real property, wherein training includes optimizing the model using a Markov chain optimization that seeks to minimize error in the model where the model is underpinned by one or more non-differentiable functions; receiving a plurality of data points associated with an input parcel of real property; and using the optimized model to generate a likelihood for the parameter for the input parcel of real property.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a collection of training data, the training data comprising collection of data points associated with a labeled set of real property parcels; training a machine learning model using the training data, the machine learning model being trained to generate a likelihood with respect to a parameter from input data associated with a specific parcel of real property, wherein training includes optimizing the model using a Markov chain optimization that seeks to minimize error in the model where the model is underpinned by one or more non-differentiable functions; receiving a plurality of data points associated with an input parcel of real property; and using the optimized model to generate a likelihood for the parameter for the input parcel of real property.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a plurality of data points associated with a specified object; using a machine learning model to generate a prediction from the obtained plurality of data points, the prediction indicating a likelihood that the object will satisfy a particular parameter and a predicted scope for the parameter, wherein the machine learning model is trained using a training set comprising a collection of data points associated with a labeled set of objects, the label indicating the particular parameter and value for each object of the training set; and based on the prediction, classifying the specified object according to a determination of whether the predicted scope satisfies a threshold value.