Abstract: Aspects of the present disclosure provide techniques for automated data classification through machine learning. Embodiments include determining, by a machine learning model, character-level embeddings of a plurality of characters from a text string. Embodiments include processing, by the machine learning model, the character-level embeddings through one or more bi-directional long short term memory (LSTM) layers. Embodiments include outputting, by the machine learning model based on the processing, a predicted label for the text string indicating a classification of the text string. Embodiments include performing, by a computing application, one or more actions based on the text string and the predicted label.

Abstract: Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Abstract: Systems and methods for scoring potential actions are disclosed. An example method may be performed by one or more processors of a system and include training a machine learning model based at least in part on a sequential database and retention data, identifying an action subsequence executed by a user, generating, for each of a plurality of potential actions, using the machine learning model, a first value indicating a probability that the user will execute the potential action immediately after executing the action subsequence, a second value indicating a probability that the user will continue to use the system if the user executes the potential action immediately after executing the action subsequence, and a confidence score indicating a likelihood that recommending the potential action to the user will result in the user continuing to use the system, the confidence score generated based on the first value and the second value.

Abstract: Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Abstract: Aspects of the present disclosure relate to real-time invoice error prevention. Embodiments include receiving a value related to an item or service during creation of an invoice by a user via a user interface, and determining a user-level mean and a user-level standard deviation related to the value based on historical invoices of the user. Embodiments include determining a global mean and a global standard deviation related to the value based on historical invoices of a plurality of users. Embodiments include selecting weights for the user-level mean, the user-level standard deviation, the global mean, and the global standard deviation based on a total number of the historical invoices of the user. Embodiments include determining an expected range for the value based on the user-level mean, the user-level standard deviation, the global mean, the global standard deviation, and the weights. Embodiments include determining that the value is outside the expected range.

Abstract: Systems and methods for scoring potential actions are disclosed. An example method may be performed by one or more processors of a system and include training a machine learning model based at least in part on a sequential database and retention data, identifying an action subsequence executed by a user, generating, for each of a plurality of potential actions, using the machine learning model, a first value indicating a probability that the user will execute the potential action immediately after executing the action subsequence, a second value indicating a probability that the user will continue to use the system if the user executes the potential action immediately after executing the action subsequence, and a confidence score indicating a likelihood that recommending the potential action to the user will result in the user continuing to use the system, the confidence score generated based on the first value and the second value.

Abstract: Certain aspects of the present disclosure provide techniques for training and using time-domain bootstrapped event prediction models to predict the occurrence of an event within a software application. An example method generally includes receiving a data set of user activity within a software application. A request to predict a likelihood of an event occurring with respect to the software application based on the user activity is received. A likelihood of the event occurring is predicted using an event prediction model. The event prediction model is generally configured to predict the likelihood of the event occurring based on a likelihood over each of a plurality of non-overlapping time windows. A likelihood of the event occurring within a first time window is conditioned on a likelihood of the event occurring within a second time window. One or more actions are taken within the software application based on the predicted likelihood.

Abstract: Identification of an optimal monochromatic energy for displaying monochromatic images is disclosed. In certain embodiments, determination of an optimal monochromatic energy may be performed by generating histograms for various monochromatic images generated based on a set of acquired multi-energy projections and by evaluating the histogram dispersion for the respective histograms.

Abstract: Identification of an optimal monochromatic energy for displaying monochromatic images is disclosed. In certain embodiments, determination of an optimal monochromatic energy may be performed by generating histograms for various monochromatic images generated based on a set of acquired multi-energy projections and by evaluating the histogram dispersion for the respective histograms.

Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.

Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.