Abstract: Disclosed are embodiments for facilitating automatic slice discovery and slice tuning for data mining in autonomous systems. In some aspects, an embodiment includes providing, by a processing device hosting a slice discovery machine learning (ML) model, input data to the slice discovery ML model, the input data corresponding to performance data of an autonomous vehicle (AV); identifying, by the slice discovery ML model, attributes of the AV and corresponding thresholds for the attributes that define a slice comprising a collection of data sharing common characteristics; and providing, by the slice discovery ML model, the attributes and the corresponding thresholds defining the slice to a slice miner to mine training data corresponding to the slice for a tailored dataset.

Abstract: Disclosed are embodiments for facilitating data mining on an edge platform using repurposed neural network models in autonomous systems. In some aspects, an embodiment includes receiving, by a processing device hosting a data source proxy head of a machine learning (ML) model deployed on an autonomous vehicle (AV), a set of features selected from raw data by a backbone network of the ML model; utilizing, by the data source proxy head, the set of features selected from the raw data as input data to a trained data source mining model of the data source proxy head; identifying, by the trained data source mining model based on the input data, a portion of the raw data to classify as mining data; and providing, by the data source proxy head, identification of the portion of the raw data as a data mining output.

Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.

Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.

Abstract: The present disclosure generally relates to autonomous vehicle (AV) prediction systems and in particular, to novel methods for training a prediction layer of the AV software stack. In some aspects, the disclosure can provide a process for receiving road data representing a plurality of entities, predicting, using a prediction layer of an autonomous vehicle (AV), a first set of future trajectories for one or more of the plurality of entities, and predicting, using a kinematics model, a second set of future trajectories for one or more of the plurality of entities. In some approaches, the process can further include steps for flagging one or more instances in the road data for which the first set of future trajectories is less accurate than the second set of future trajectories. Systems and machine-readable media are also provided.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and methods are provided for investigation network activities. Network activity information may be accessed. The network activity information may describe for an individual (1) respective relationship with one or more persons; and (2) respective activity status information indicating whether a given person has engaged in a particular activity. A network activity graph may be generated based on the network activity information. The network activity graph may include two or more nodes representing the individual and the one or more persons. Connections between the nodes may represent the respective relationships between the individual and the one or more persons. Data corresponding to the network activity graph may be presented through an interface.

Abstract: Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots.

Abstract: Systems and methods are provided for performing context-based keyword searching using a search bar. Based on search terms received via the search bar, the system may be configured to provide suggested search parameters to associate with that search term. The suggested search parameters may each include a type of data and/or a filter to associate with the search term (e.g., name, phone number, date of birth, etc.). The one or more suggested search parameters may be identified based on the search term itself, a list of possible types of data or filters, a preliminary search of one or more datasets, a record of one or more previous searches performed, requirements associated with one or more searchable datasets, the format of user input received via the search bar, and/or one or more other factors.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and methods are provided for identifying relevant information for an entity, referred to as a seed entity. A plurality of search queries can be generated each comprising a property of a seed entity or one of the entities associated with the seed entity (seed-linked entities). Preferably, a collection of search queries includes ones representing different properties of the seed entity and properties of different seed-linked entities. Optionally, the collection of search queries is optimized to reduce search burden. Searches can then be conducted with the search queries in one or more data sources to obtain a plurality of search results, wherein each search result comprises a hit entity and one or more entities associated with the hit entity (hit-linked entity).

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and methods are disclosed for systems and user interfaces for rapid analysis of viewership information. One of the methods includes accessing databases storing viewership information associated with segments, with each segment being associated with common features of viewers. Measures of association between the segment and content items are maintained for each segment. An interactive user interface is presented via a user device, the interactive user interface enabling creation of a customized viewing audience. The interactive user interface receives user input indicating a segment, identifies similar segments based on associations between features of the segment and of other segments, and presents the identified segments. Analysis information associated with the segments is presented for at least one of the one or more segments, with the segments being included in the customized viewing audience.

Abstract: Systems and methods are provided for performing context-based keyword searching using a search bar. Based on search terms received via the search bar, the system may be configured to provide suggested search parameters to associate with that search term. The suggested search parameters may each include a type of data and/or a filter to associate with the search term (e.g., name, phone number, date of birth, etc.). The one or more suggested search parameters may be identified based on the search term itself, a list of possible types of data or filters, a preliminary search of one or more datasets, a record of one or more previous searches performed, requirements associated with one or more searchable datasets, the format of user input received via the search bar, and/or one or more other factors.

Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and methods are provided for investigation network activities. Network activity information may be accessed. The network activity information may describe for an individual (1) respective relationship with one or more persons; and (2) respective activity status information indicating whether a given person has engaged in a particular activity. A network activity graph may be generated based on the network activity information. The network activity graph may include two or more nodes representing the individual and the one or more persons. Connections between the nodes may represent the respective relationships between the individual and the one or more persons. Data corresponding to the network activity graph may be presented through an interface.

Abstract: Systems and methods are provided for performing context-based keyword searching using a search bar. Based on search terms received via the search bar, the system may be configured to provide suggested search parameters to associate with that search term. The suggested search parameters may each include a type of data and/or a filter to associate with the search term (e.g., name, phone number, date of birth, etc.). The one or more suggested search parameters may be identified based on the search term itself, a list of possible types of data or filters, a preliminary search of one or more datasets, a record of one or more previous searches performed, requirements associated with one or more searchable datasets, the format of user input received via the search bar, and/or one or more other factors.

Abstract: Systems and methods are provided for identifying and compiling information relating to an entity for investigative analysis. The system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to search, in one or more data sources, with a plurality of known characteristics of an entity to obtain a first plurality of records, identify from the first plurality of records a subset of records that match the known characteristics with a substantial confidence, compile the subset of records to form a unified record representing the entity and conduct a second search with information from the unified record to obtain a second plurality of search results.