Abstract: Systems, methods, articles of manufacture, and computer program products to: train a prediction model using a machine learning process, the prediction model configured to estimate whether further application of a hyperparameter tuning technique will cause an improvement in at least one of the hyperparameters; select the hyperparameters using the tuning technique; apply the prediction model to determine if further adjustment of the hyperparameters is likely to improve the success metric; and terminate the tuning technique when: accuracy of the prediction model in predicting improvement in a hyperparameter is above a predetermined accuracy threshold, and the prediction model predicts that further application of the tuning technique will not result in an improvement to the hyperparameter; or the accuracy of the prediction model in predicting improvement in the parameter is below the predetermined accuracy threshold, and an accuracy of hyperparameter adjustment is determined to be below a predetermined adjustment

Abstract: Various embodiments are directed to a system that utilizes regular expression (regex) to recognize at least portions of characters, words, text, numbers, etc. in a structured or unstructured dataset, any patterns associated therewith, and/or similarities between the determined patterns. In examples, a regex-based pattern recognition platform may receive a dataset and determine whether at least a first regex pattern and a second regex pattern can be identified. The occurrences of the first and second regex patterns and the frequency of those occurrences may reveal something about the dataset itself or any patterns contained therein.

Abstract: Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.

Abstract: Exemplary embodiments may use word embeddings to enhance scanning of programming code scripts for sensitive subject matter, such as confidential subject matter. The scanning may be performed by a neural network in some exemplary embodiments. The neural network initially may be trained on a corpus of programming code scripts to identify keywords relating to sensitive subject matter, such as passwords, tokens or credentials. The neural network may not only identify instances of the keywords but also may identify related terms as well. The output of the scan may be a ranked list of terms in the programming code script that may relate to sensitive subject matter.

Abstract: Techniques for providing a real-time service that protects personal data of clients from customer service agents are provided. Customer data that includes personal data indicative of sensitive information of a customer can be received from the customer. The personal data within the received customer data can be detected and a token that does not include the sensitive information of the customer can be generated. The personal data and the generated token can be stored along with data indicating a relationship between the token and the personal data. The personal data in the received customer data can be replaced by the token to form modified customer data. The modified customer data can be provided to a customer service representative. The token within the modified customer data can later be detected and associated with the personal data without revealing the personal data to the customer service representative.

Abstract: Systems, methods, articles of manufacture, and computer program products to: train a prediction model using a machine learning process, the prediction model configured to estimate whether further application of a hyperparameter tuning technique will cause an improvement in at least one of the hyperparameters; select the hyperparameters using the tuning technique; apply the prediction model to determine if further adjustment of the hyperparameters is likely to improve the success metric; and terminate the tuning technique when: accuracy of the prediction model in predicting improvement in a hyperparameter is above a predetermined accuracy threshold, and the prediction model predicts that further application of the tuning technique will not result in an improvement to the hyperparameter; or the accuracy of the prediction model in predicting improvement in the parameter is below the predetermined accuracy threshold, and an accuracy of hyperparameter adjustment is determined to be below a predetermined adjustment

Abstract: Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

Abstract: Techniques for providing a real-time service that protects personal data of clients from customer service agents are provided. Customer data that includes personal data indicative of sensitive information of a customer can be received from the customer. The personal data within the received customer data can be detected and a token that does not include the sensitive information of the customer can be generated. The personal data and the generated token can be stored along with data indicating a relationship between the token and the personal data. The personal data in the received customer data can be replaced by the token to form modified customer data. The modified customer data can be provided to a customer service representative. The token within the modified customer data can later be detected and associated with the personal data without revealing the personal data to the customer service representative.

Abstract: Techniques for protecting passwords and/or password entry by a user are provided. User identification data for a user can be received from a remote computing device. An identity of the user can be determined based on the user identification data. A password for the user can be determined. A modified keyboard configuration associated with the user can be determined. A request can be transmitted to the remote computing device for the password for the user based on the modified keyboard configuration. A modified password from the remote computing device can be received. A converted password based on the modified password and the modified keyboard configuration can be determined. The converted password can be compared to the password for the user. The user can be authorized when the converted password matches the password for the user.

Abstract: Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

Abstract: Various embodiments are generally directed to techniques for creating and utilizing multidimensional embedding spaces for data objects, such as to condition the data for input to a neural network, for instance. Some embodiments are particularly directed to converting semi-structured data, such as a set of data objects, into object vector sets mapped to a multidimensional embedding space. In many embodiments, an embedding space for a set of data objects may be customized with a set of dimensions that correspond to various characteristics of the set of data objects. These and other embodiments are described and claimed.

Abstract: Systems, methods, and computer program products to transmit, by a web browser to a web server, a hypertext transfer protocol request for a web page at a first uniform resource identifier (URI). The web browser may receive, from the web server via, the web page and metadata of a plurality of candidate advertisements, the plurality of candidate advertisements determined based on a master machine learning (ML) model. A client ML model executing in the web browser may process the received metadata, the client ML model trained based on prior interactions between one or more users of the web browser and a plurality of previously displayed advertisements. The client ML model may determine based on the processing, a first candidate advertisement of the plurality of candidate advertisements to display in the web browser with the received web page.

Abstract: Various embodiments are generally directed to techniques for creating and utilizing multidimensional embedding spaces for data objects, such as to condition the data for input to a neural network, for instance. Some embodiments are particularly directed to converting semi-structured data, such as a set of data objects, into object vector sets mapped to a multidimensional embedding space. In many embodiments, an embedding space for a set of data objects may be customized with a set of dimensions that correspond to various characteristics of the set of data objects. These and other embodiments are described and claimed.

Abstract: Systems, methods, and computer program products to transmit, by a web browser to a web server, a hypertext transfer protocol request for a web page at a first uniform resource identifier (URI). The web browser may receive, from the web server via, the web page and metadata of a plurality of candidate advertisements, the plurality of candidate advertisements determined based on a master machine learning (ML) model. A client ML model executing in the web browser may process the received metadata, the client ML model trained based on prior interactions between one or more users of the web browser and a plurality of previously displayed advertisements. The client ML model may determine based on the processing, a first candidate advertisement of the plurality of candidate advertisements to display in the web browser with the received web page.

Abstract: Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.

Abstract: Various embodiments are generally directed to techniques to reduce inputs of a machine learning model (MLM) and increase path efficiency as a result. A method for reducing an MLM includes: receiving a machine learning (ML) dataset, partitioning the ML dataset into a first dataset, a second dataset, a third dataset, and a fourth dataset, training, validating, and testing the MLM using one or more of the first dataset, the second dataset, and the third dataset, after testing the MLM, automatically ranking an importance associated with each input of the MLM using the fourth dataset, and reducing a plurality of inputs of the MLM based on the automatic ranking.

Abstract: Techniques to improve a schedule using optimization are described. Some described techniques improve the schedule using optimization upon a user's travel booking operation and/or in response to changes in the user's relationships. The techniques include an apparatus, a method, and a computer-readable medium configured to process relationship data associated with potential candidates for a set of meetings in a schedule, relationship data corresponding to interaction indicia with each potential candidate, generate, from the relationship data, a connectivity network comprising links with the potential candidates, each link of the links corresponding to a relevance value between a user and a specific potential candidate, and populate, via an optimization unit, open meeting spaces in the schedule with meeting data based upon the connectivity network and availability data of the potential candidates, the schedule being configured to substantially maximize relevancy of the set of meetings.

Abstract: Various embodiments are generally directed to techniques for creating and utilizing multidimensional embedding spaces for data objects, such as to condition the data for input to a neural network, for instance. Some embodiments are particularly directed to converting semi-structured data, such as a set of data objects, into object vector sets mapped to a multidimensional embedding space. In many embodiments, an embedding space for a set of data objects may be customized with a set of dimensions that correspond to various characteristics of the set of data objects. These and other embodiments are described and claimed.

Abstract: Various embodiments are generally directed to techniques to reduce inputs of a machine learning model (MLM) and increase path efficiency as a result. A method for reducing an MLM includes: receiving a machine learning (ML) dataset, partitioning the ML dataset into a first dataset, a second dataset, a third dataset, and a fourth dataset, training, validating, and testing the MLM using one or more of the first dataset, the second dataset, and the third dataset, after testing the MLM, automatically ranking an importance associated with each input of the MLM using the fourth dataset, and reducing a plurality of inputs of the MLM based on the automatic ranking.

Abstract: Systems, methods, articles of manufacture, and computer program products to train a generation model to determine whether a search space portion is likely to provide hyperparameters that improve a success metric; sequentially select at least a subset of multiple search space portions; for each selected search space portion, generate hyperparameters from the search space portion, perform hyperparameter tuning with the hyperparameters to determine whether the hyperparameters improved the success metric, apply the generation model based on whether the success metric is improved to determine whether the search space portion is likely to provide further hyperparameters that improve the success metric, and rule out the search space portion from providing further hyperparameters in response to determining that the search space portion is unlikely to provide further hyperparameters that improve the success metric; and terminate the performance of hyperparameter tuning when all search space portions are ruled out.