Abstract: Described are systems and methods for generating and displaying ratings for points of interest in a region. A method for generating ratings includes receiving transaction data from a plurality of points of interest, the transaction data representing financial transactions between a plurality of individuals and each point of interest of the plurality of points of interest. A method also includes automatically generating, for each point of interest of the plurality of points of interest, at least one point of interest rating based at least partially on the transaction data for the point of interest. A method further includes, in response to a selection of a chosen point of interest of the plurality of points of interest, providing to at least one user the at least one point of interest rating associated with the chosen point of interest.

Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.

Abstract: Described are systems and methods for generating and displaying ratings for points of interest in a region. A method for generating ratings includes receiving transaction data from a plurality of points of interest, the transaction data representing financial transactions between a plurality of individuals and each point of interest of the plurality of points of interest. A method also includes automatically generating, for each point of interest of the plurality of points of interest, at least one point of interest rating based at least partially on the transaction data for the point of interest. A method further includes, in response to a selection of a chosen point of interest of the plurality of points of interest, providing to at least one user the at least one point of interest rating associated with the chosen point of interest.

Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.

Abstract: A client device retrieves a diarization model. The diarization model has been trained to determine whether there is a change of one speaker to another speaker within an audio sequence. The client device receives enrollment data from each speaker of a group of speakers who are participating in an audio conference. The client device obtains an audio segment from a recording of the audio conference. The client device identifies one or more speakers for the audio segment by applying the diarization model to a combination of the enrollment data and the audio segment.