A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.

A method and system for evaluating performance of operation resources using Artificial Intelligence (AI) is disclosed. In some embodiments, the method includes receiving, each of a plurality of performance parameters associated with a set of operation resources. The method further includes determining a set of features for each of the plurality of performance parameters. The method further includes creating one or more feature vectors corresponding to each of the plurality of performance parameters. The one or more feature vectors are created based on a first pre-trained machine learning model. The method further includes assessing the one or more feature vectors, based on the first pre-trained machine learning model and classifying the set of operation resources into one of a set of performance categories based on the assessing of the one or more feature vectors. The method further includes evaluating performance of at least one of the set of operation resources.

Methods and systems for training a model using machine learning for automatically distributing medical imaging studies to radiologists. One method includes receiving one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image. The method further includes receiving radiologist metadata for each one of the plurality of radiologists, generating a state representation of the image metadata and the radiologist metadata, and providing the state representation to the model. The method further includes assigning, with the model, at least one of the one or more medical images to one of the plurality of radiologists, calculating feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, and adjusting the model based on the feedback.

The present invention discloses an indoor Electronic Traveling Aid (ETA) system for blind and visually impaired (BVI) people. The system comprises a headband, intuitive tactile display with myographic (EMG) feedback, controller, and server-based methods corresponding to three operation modalities. In 1.sup.st modality, sighted users mark routes, map navigational directions, and create semantic comments for BVIs. This information of routes is continuously collected and estimated in ETA servers. In the 2.sup.nd modality, BVIs choose the routes from servers, thereby, are supplied with real-time navigational guidance. Also, an EMG interface is used, where the user's facial muscles are enabled is to send commands to the ETA system. In the 3.sup.rd modality, BVIs receive real-time audio guidance in complex or unforeseen situations: ETA provides a crowd-assisted interface and real-time sensory (e.g., video) data, where crowd-assistants analyze the situation and help the BVI to navigate.

In various examples, machine learning of encoding parameter values for a network is performed using a video encoder. Feedback associated with streaming video encoded by a video encoder over a network may be applied to an MLM(s). Using such feedback, the MLM(s) may predict a value(s) of an encoding parameter(s). The video encoder may then use the value to encode subsequent video data for the streaming. By using the video encoder in training, the MLM(s) may learn based on actual encoded parameter values of the video encoder. The MLM(s) may be trained via reinforcement learning based on video encoded by the video encoder. A rewards metric(s) may be used to train the MLM(s) using data generated or applied to the physical network in which the MLM(s) is to be deployed and/or a simulation thereof. Penalty metric(s) (e.g., the quantity of dropped frames) may also be used to train the MLM(s).

A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

A method comprising receiving first data representative of a query. A representation of the query is generated using an encoder neural network and the first data. Words for a rephrased version of the query are selected from a set of words comprising a first subset of words comprising words of the query and a second subset of words comprising words absent from the query. Second data representative of the rephrased version of the query is generated.

A reinforcement learning method and recognition apparatus includes: obtaining a structure graph, where the structure graph includes structure information that is of an environment or the intelligent agent and that is obtained through learning; inputing a current state of the environment and the structure graph to a policy function of the intelligent agent, where the policy function is used to generate an action in response to the current state and the structure graph, and the policy function of the intelligent agent is a graph neural network; outputing the action to the environment by using the intelligent agent; obtaining, from the environment by using the intelligent agent, a next state and reward data in response to the action; training the intelligent agent through reinforcement learning based on the reward data.

[Problem] To provide an automatic driving robot control device and control method that enable a vehicle to be operated smoothly while also being caused to conform to a command vehicle speed with high accuracy.

[Solution] The present invention provides an automatic driving robot (drive robot) 4 control device 10 that controls the automatic driving robot 4, which is installed in a vehicle 2 and causes the vehicle 2 to run, such that the vehicle 2 runs in accordance with a defined command vehicle speed, wherein the automatic driving robot 4 control device 10 is provided with: a running state acquisition unit 22 that acquires a running state of the vehicle 2 including a vehicle speed and the command vehicle speed; an operation content inference unit 31 that infers, on the basis of the running state, an operation sequence, which is a sequence of operations of the vehicle 2 at a plurality of times in the future that causes the vehicle 2 to run in accordance with the command vehicle speed, by using an operation inference learning model 40 that was trained by machine learning to infer the operation sequence; and a vehicle operation control unit 23 that extracts, from each of the operation sequences inferred a plurality of times in the past, the operations corresponding to a control time for subsequently controlling the automatic driving robot 4, calculates a weighted sum of these extracted plurality of operations to calculate a final operation value, generates, on the basis of the final operation value, a control signal for controlling the automatic driving robot 4, and transmits the control signal to the automatic driving robot 4.

In an embodiment, a method includes: receiving first radar data from a millimeter-wave radar sensor; receiving a set of hyperparameters with a radar processing chain; generating a first radar processing output using the radar processing chain based on the first radar data and the set of hyperparameters; updating the set of hyperparameters based on the first radar processing output using a hyperparameter selection neural network; receiving second radar data from the millimeter-wave radar sensor; and generating a second radar processing output using the radar processing chain based on the second radar data and the updated set of hyperparameters.