Abstract: Methods and systems for providing a machine learning model that can perform predictions based on incomplete input values are presented. The machine learning model includes multiple input layers of input nodes, where input nodes from different input layers can be connected with each other. Based on the connections among the input nodes, certain input values can be inferred from other input values. When a request is received, it is determined which input values are available and which input values are missing. Based on which input values are available, the machine learning model is modified by masking a subset of connections among nodes in the input layers. The modified machine learning model is then configured to infer the missing input values from the available input values, and to provide an output based on the available input values and the inferred input values. The request is processed based on the output.

Abstract: Techniques are disclosed relating to assessing technology activity using image-based machine learning algorithms. A computer system may access a data set that includes a plurality of parameters (e.g., technologies) for an item (e.g., a web-based interface). The plurality of parameters may correspond to a plurality of time intervals. The computer system may generate a two-dimensional graphical representation of the data set. A first dimension of the graphical representation may be indicative of values of the plurality of parameters at different time intervals and a second dimension of the graphical representation may be indicative of a time period that includes the plurality of time intervals. At least one characteristic of the data set may be determined by inputting the graphical representation of the data set to a trained machine learning module. The trained machine learning module may implement an image-based learning algorithm.

Abstract: Various techniques for determining risk assessment predictions and decisions are disclosed. Certain disclosed techniques include the implementation of decision-tree based models in determining predictions of risk for an operation based on an input dataset. The disclosed techniques include pruning decision trees to compensate for deprecation of variables from the input dataset. Decision trees may be pruned at nodes associated with the deprecated variables to inhibit the decision trees from breaking down during operation on an input dataset having deprecated variables.

Abstract: Various techniques for determining risk assessment predictions and decisions are disclosed. Certain disclosed techniques include the implementation of neural network models in determining predictions of risk for an operation based on an input dataset. The disclosed techniques include training the neural network models to compensate for deprecation of variables from the input dataset. The neural network models may be trained to be robust in view of deprecated variables by dropping variables from the input space during training of the neural network models.

Abstract: An electronic network, such as an autonomous vehicle, an Internet of Things (IoT) network, a medical device system, or a network security system, is accessed. The electronic network contains a plurality of electronic modules, such as electronic sensors, that each have a predefined functionality. Data associated with the electronic modules is collected over a predefined period of time. A matrix is constructed based on the collected data. Based on the matrix, a redundant electronic module is detected from the plurality of electronic modules. A functionality of the redundant electronic module is replicated collectively by one or more other electronic modules of the plurality of electronic modules. A value added by the redundant electronic module to the electronic network is evaluated. Based on the evaluated value, a determination is made as to whether the redundant electronic module should be removed from the electronic network.

Abstract: Techniques are disclosed relating to machine learning techniques for performing user authentication based on the manner in which a user interacts with a client device, including the use of Siamese networks to detect unauthorized use of a device and/or account. In some embodiments, a server system may receive a request to authorize a transaction associated with a user account. The request may include transaction details and, separate from those transaction details, interaction data indicative of a manner in which a requesting user interacts with a client device during a user session. The server system may apply a machine learning model to the interaction data to create an encoding value that is based on the manner in when the requesting user interacts with the client device during the user session. The server system may then compare the encoding value to a reference encoding value and, based on the comparison, determine whether to authorize the transaction.

Abstract: A first schema accessed is associated with a plurality of entities that are participants of an electronic system. Each entity has a corresponding attribute. Each of a first subset of the entities has a respective attribute value below a threshold. Each of a second subset of the entities has a respective attribute value above the threshold. According to the first schema, it is determined that data and/or transactions associated with the first and second subsets of the entities are processed using a first and a second model, respectively. A second schema is generated by softening the predefined threshold such that according to the second schema, data and/or transactions associated with the first subset of the entities and data and/or transactions associated with the second subset of the entities are each processed using both the first model and the second model. The second schema is implemented in the electronic system.

Abstract: A computing device generates a first token for first data content that is associated with a first relationship and a second relationship, and a second token for second data content that is associated with the first relationship and a third relationship, such that the first token and second token are generated based on a frequency of use of data values included in the first and the second data content. The computing device calculates a first similarity score of data values from third data content that is associated with the second relationship and a fourth relationship with data values from fourth data content that is associated with the third relationship and the fourth relationship in response to the first and second token matching. The computing device then performs, in response to the first similarity score satisfying a similarity threshold, a first modification to any of the data content.

Abstract: Systems and methods for anonymizing content suggestive of a particular characteristic while preserving relevant content are disclosed.

Abstract: Computer system security and efficiency of processing operations may be improved using techniques that are described relating to analyzing user actions based on time of day of occurrence, and using time of a day as a factor in determining whether a particular action should be allowed or disallowed. Past action times can be transformed to a two-dimensional representation using a radial time schema that avoids discontinuity. A probability distribution can indicate a likelihood of whether a new action fits a previous pattern. If a new user action is relatively unlikely due to time of day, the new user action might be denied/prevented from completing, thus enhancing computer system security and avoiding unnecessary computational processing costs.

Abstract: Techniques are described relating to analyzing user transactions based on time of day of occurrence, and using time of a day as a factor in determining whether a new transaction should be allowed or disallowed. People may have particular tendencies to engage in transaction at certain times of a day. When a new transaction occurs that does not fit a previous pattern, this can indicate someone else has gained access to the account. Past times of transactions can be transformed to a two-dimensional representation that avoids discontinuity. A smoothed probability distribution can indicate a likelihood of whether a new transaction fits previous patterns. If a new transaction is unlikely due to time of day, the new transaction might be denied/prevented from completing. Denial of the transaction may also be based on additional factors besides the time of day.

Abstract: Techniques are disclosed relating to training a machine learning model to understand one or more rules without explicitly executing the rule. In some embodiments, a computer system generates synthetic samples for a trained machine learning model usable to make a classification decision, where the synthetic samples are generated from a rule and a set of existing samples. In some embodiments, the set of existing samples are selected based on exceeding a confidence threshold for the classification decision. In some embodiments, the computer system retrains the trained machine learning model using the synthetic samples.

Abstract: Systems and methods are disclosed for managing data objects that include text content that are stored in a database. The management of text objects includes determining that a condition for a data object associated with a user has been satisfied. In response, a relevancy score for the data object is generated. The relevancy score is based on the text content of the data object and a density estimation model associated with the user. The density estimation is generated using a plurality of data objects that each include text content and that are associated with a plurality of users of a service associated with the data objects, and using a set of the plurality of data objects that are associated with the user. Irrelevancy actions or relevancy actions may be performed to the data object based on the relevancy score.

Abstract: Methods and systems are presented for imputing missing data items within a first dataset based on data associated with a second dataset that is the nearest neighbor of the first dataset. A first mapping model is configured to map data subsets corresponding to a first data source to first positions in a multi-dimensional space. A second mapping model is configured to map data subsets corresponding to a second data source to second positions in the multi-dimensional space. The first and second mapping models are trained together to reduce a distance between positions mapped by the first and second mapping models based on corresponding data subsets that belong to the same entity. A nearest neighbor dataset to the first dataset is identified based on the first and second mapping models. Data associated with the nearest neighbor dataset is used to impute the missing data items of the first dataset.

Abstract: Techniques are described relating to identifying a country (or other item) associated with an individual based on the individual's name. These techniques rely on machine learning and artificial intelligence adaptions, according to various embodiments, and allow for better identification of country than some alternative techniques. Specifically, unsupervised machine learning techniques (e.g. using a word2vec based algorithm) allow for the handling of noisy data, which can be a significant difficulty in attempting to associate a person's name to a particular country, where it may be quite difficult or even impossible to train a supervised machine learning model that can effectively make decisions on how to associate an unknown person to a particular country. Accordingly, this disclosure includes techniques related to unsupervised machine learning that are particularly helpful for solving this problem, including using a training data set that is prepared by adding country codes (or another identifier) to names.

Abstract: Methods and systems are presented for identifying different users who share a user account with an online service provider and dynamically processing transactions for the user account differently based on which user initiates the transaction request. In some embodiments, an account decomposition system may decompose the user account into distinct users who share the user account. The account decomposition system may identify different users who are sharing a user account by analyzing past transactions associated with the user account and different user devices that were used to conduct the past transactions. The account decomposition system may determine different user profiles for the different users, and may use the different user profiles to process incoming transaction requests initiated by different users of the user account.

Abstract: Systems and methods for query-based recommendation systems using machine learning-trained classifiers are provided. A service provider server receives, from a communication device through an application programming interface, a query in an interaction between the server provider server and the communication device. The service provider server generates a vector of first latent features from a set of first visible features associated with the query using a machine learning-trained classifier. The service provider server generates a likelihood scalar value indicating a likelihood of the query is answered by a candidate user in a set of users using a combination of the vector of first latent features and a vector of second latent features. The service provider server provides, to the communication device through the application programming interface, a recommendation message as a response to the query, where the recommendation message includes the likelihood scalar value and an indication of the candidate user.

Abstract: Systems and methods are disclosed for managing data objects that include text content that are stored in a database. The management of text objects includes determining that a condition for a data object associated with a user has been satisfied. In response, a relevancy score for the data object is generated. The relevancy score is based on the text content of the data object and a density estimation model associated with the user. The density estimation is generated using a plurality of data objects that each include text content and that are associated with a plurality of users of a service associated with the data objects, and using a set of the plurality of data objects that are associated with the user. Irrelevancy actions or relevancy actions may be performed to the data object based on the relevancy score.

Abstract: Techniques are disclosed relating to machine learning techniques for performing user authentication based on the manner in which a user interacts with a client device, including the use of Siamese networks to detect unauthorized use of a device and/or account. In some embodiments, a server system may receive a request to authorize a transaction associated with a user account. The request may include transaction details and, separate from those transaction details, interaction data indicative of a manner in which a requesting user interacts with a client device during a user session. The server system may apply a machine learning model to the interaction data to create an encoding value that is based on the manner in when the requesting user interacts with the client device during the user session. The server system may then compare the encoding value to a reference encoding value and, based on the comparison, determine whether to authorize the transaction.

Abstract: A machine learning process includes first, second, third and fourth phases. The first phase includes accessing geographical locations and economic traits for merchants. First merchants have offline locations. Second merchants have no offline locations. The second phase includes labeling third merchants as having offline locations and labeling fourth merchants as having no offline locations. The third phase includes training a machine learning model via the economic trait data of the first, second, third, and fourth merchants. A first probability of having the offline location and a second probability of having no offline location are determined via the trained model and for each of the remaining merchants. Fifth merchants whose predicted first probability exceeds a first predefined threshold are labeled as having offline locations. Sixth merchants whose predicted second probability exceeds a second predefined threshold are labeled as having no offline locations.