Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an optimal feature point in a continuous domain for a group of agents. A computer-implemented system obtains, for each of a plurality of agents, respective training data that comprises a respective utility score for each of a plurality of discrete points in the continuous domain. The system trains, for each of the plurality of agents and on the respective training data for the agents, a respective neural network that is configured to receive an input comprising a point in the continuous domain and to generate as output a predicted utility score for the agent at the point. And the system identifies the optimal point by optimizing an approximation of the shared outcome function that is defined by, for any given point in the continuous domain, a combination of the predicted utility scores generated by the respective neural networks for each of the plurality of agents by processing an input comprising the given point.

The present invention proposes a method for distributing labeling work, wherein a computing apparatus uses a deep learning model performing bounding box labeling work, in order to find the position of an object included in an image and classify the type of the object, the method comprising the steps of: obtaining a predetermined image including at least one object by the computing apparatus; performing calculation, by the computing apparatus, while passing the predetermined image through the deep learning model, to obtain i) the coordinates of a bounding box with respect to the at least one object, ii) a classification value indicating the type of the at least one object, and iii) a loss value indicating the degree of error of the obtained bounding box; and determining, by the computing apparatus, difficulty levels of labeling work on the basis of the loss value and the classification value, and distributing the labeling work to workers according to the determined difficulty levels.

Systems and method for machine-learning assisted road sign content prediction and machine learning training is disclosed. A sign detector model processes images or video with road signs. A visual attribute prediction model extracts visual attributes of the sign in the image. The visual attribute prediction model can communicate with a knowledge graph reasoner to validate the visual attribute prediction model by applying various rules to the output of the visual attribute prediction model. A plurality of potential sign candidates are retrieved that match the visual attributes of the image subject to the visual attribute prediction model, and the rules help to reduce the list of potential sign candidates and improve accuracy of the model.

A method includes receiving, by a computing device, training data to train a neural network, wherein the training data comprises a plurality of inputs and a plurality of corresponding labels. The method also includes mapping, by a representation learner of the neural network, the plurality of inputs to a plurality of feature vectors. The method additionally includes training a kernelized classification layer of the neural network to perform nonlinear classification of an input feature vector into one of a plurality of classes, wherein the kernelized classification layer is based on a kernel which enables the nonlinear classification, and wherein the kernel is selected from a space of positive definite kernels based on application of a nonlinear softmax loss function to the plurality of feature vectors and the plurality of corresponding labels. The method further includes outputting a trained neural network comprising the representation learner and the trained kernelized classification layer.

This application provides a method and apparatus for constructing a recommendation model. The method includes: aggregating a plurality of feature tables corresponding to each application scenario in a recommendation project to obtain an aggregated feature table, the recommendation project including a plurality of application scenarios in a one-to-one correspondence with a plurality of recommendation indicators of a to-be-recommended item, each application scenario having a recommendation model to predict a corresponding recommendation indicator; receiving a corresponding user feature and item feature from the aggregated feature table based on a user identifier and an item identifier included in a sample data table, and stitching the features with the sample data table, to form a training sample set; and training the recommendation model of the application scenario based on the training sample set, the trained recommendation model being capable of fitting a user feature and an item feature in the training sample set.

This application provides a method and apparatus for constructing a recommendation model. The method includes: aggregating a plurality of feature tables corresponding to each application scenario in a recommendation project to obtain an aggregated feature table, the recommendation project including a plurality of application scenarios in a one-to-one correspondence with a plurality of recommendation indicators of a to-be-recommended item, each application scenario having a recommendation model to predict a corresponding recommendation indicator; receiving a corresponding user feature and item feature from the aggregated feature table based on a user identifier and an item identifier included in a sample data table, and stitching the features with the sample data table, to form a training sample set; and training the recommendation model of the application scenario based on the training sample set, the trained recommendation model being capable of fitting a user feature and an item feature in the training sample set.

Systems and methods for determining input data is out-of-domain of an AI (artificial intelligence) based system are provided. Input data for inputting into an AI based system is received. An in-domain feature space of the AI based system and an out-of-domain feature space of the AI based system are modelled. The in-domain feature space corresponds to features of data that the AI based system is trained to classify. The out-of-domain feature space corresponds to features of data that the AI based system is not trained to classify. Probability distribution functions in the in-domain feature space and the out-of-domain feature space are generated for the input data and for the data that the AI based system is trained to classify. It is determined whether the input data is out-of-domain of the AI based system based on the probability distribution functions for the input data and for the data that the AI based system is trained to classify.

A community question-answer (CQA) web site answer sorting method and system combined with active learning. The sorting method comprises: step S1, performing question-answer data representation and modeling; and step S2, constructing a training set in combination with active learning, and predicting a sorting relationship of candidate question-answer pairs. Also provided is a community question-answer website answer sorting system combined with active learning. CQA website question-answer data is first represented and modeled, interference to answers sorting caused by long tail distribution of the community data is solved by means of a long tail factor, and an attention mechanism is introduced in a convolutional neural network to relieve a semantic gap problem among question-answer texts. Then, an unlabeled training set is also constructed, a sample is additionally selected from the unlabeled training set and labeled, and an answer sorting model is trained again after labeling results are merged.

Systems/techniques that facilitate learning-based clean data selection are provided. In various embodiments, a system can access a raw dataset. In various aspects, the system can select, via execution of a data selection machine learning model, a clean dataset from the raw dataset. In various instances, the system can train a target machine learning model to perform a target task based on the clean dataset. In various aspects, the clean dataset can include candidate-annotation groupings that are in the raw dataset and that are determined by the data selection machine learning model to be suitable for training of the target machine learning model, and the clean dataset can exclude candidate-annotation groupings that are in the raw dataset and that are determined by the data selection machine learning model to not be suitable for training of the target machine learning model.

Methods and systems provide for intelligent notification of multitasking options during a communication session. The system receives information associated with a number of past requested events associated with a user of a communication platform, and a user behavioral profile associated with the user. The system receives notification of a requested event for the user. The system then deploys an AI model to analyze the user behavioral profile with respect to the requested event and the one or more past events, and generate, based on the analysis, prediction classification scores for one or more multitasking activities which can be performed by the user concurrently to attending the requested event. Finally, the system provides, based on the prediction classification scores, notification of at least a subset of the one or more multitasking activities which can be performed by the user concurrently to attending the requested event.