Abstract: Systems and techniques that facilitate parameterized neighborhood memory adaptation for semantic role labeling are provided. In various embodiments, a system can comprise a receiver component that can access a semantic role labeling model trained on a training dataset. In various aspects, the system can further comprise an execution component that can retrain a labeler of the semantic role labeling model based on a set of neighborhood parameters learned from the training dataset. In various instances, the execution component can execute, after retraining, the semantic role labeling model on an inputted sentence.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to regularizing semantic similarity relationships relative to a pair of languages. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a computation component that generates a transformation comprising a semantic similarity relationship between detected semantic labels of a pair of languages.

Abstract: Systems and methods for training an object detection network are described. Embodiments train an object detection network using a labeled training set, wherein each element of the labeled training set includes an image and ground truth labels for object instances in the image, predict annotation data for a candidate set of unlabeled data using the object detection network, select a sample image from the candidate set using a policy network, generate a labeled sample based on the selected sample image and the annotation data, wherein the labeled sample includes labels for a plurality of object instances in the sample image, and perform additional training on the object detection network based at least in part on the labeled sample.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to regularizing semantic similarity relationships relative to a pair of languages. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a computation component that generates a transformation comprising a semantic similarity relationship between detected semantic labels of a pair of languages.

Abstract: Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Abstract: Systems and techniques that facilitate parameterized neighborhood memory adaptation for semantic role labeling are provided. In various embodiments, a system can comprise a receiver component that can access a semantic role labeling model trained on a training dataset. In various aspects, the system can further comprise an execution component that can retrain a labeler of the semantic role labeling model based on a set of neighborhood parameters learned from the training dataset. In various instances, the execution component can execute, after retraining, the semantic role labeling model on an inputted sentence.

Abstract: Systems and methods for training an object detection network are described. Embodiments train an object detection network using a labeled training set, wherein each element of the labeled training set includes an image and ground truth labels for object instances in the image, predict annotation data for a candidate set of unlabeled data using the object detection network, select a sample image from the candidate set using a policy network, generate a labeled sample based on the selected sample image and the annotation data, wherein the labeled sample includes labels for a plurality of object instances in the sample image, and perform additional training on the object detection network based at least in part on the labeled sample.

Abstract: Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Abstract: A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Abstract: A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

Abstract: A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Abstract: A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

Abstract: A method for operating a ride-share-enabled vehicle includes determining a target location of the ride-share-enabled vehicle, determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, if any, based on the determined target location of the ride-share-enabled vehicle, determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm, and causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.