A machine learning system performs transfer learning to output a trained model by performing training using a parameter of a pre-trained model by using a given dataset and a given pre-trained model. The machine learning system includes a dataset storage unit that stores one or more datasets, and a first training unit that performs training using a dataset stored in the dataset storage unit to generate the pre-trained model, and stores the generated pre-trained model in a pre-trained model database. The dataset storage unit stores tag information including any one or more of domain information indicating a target object of data included in a dataset to be stored, class information indicating a class included in data, and data acquisition condition information related to an acquisition condition of data and a dataset in a manner that the tag information and the dataset are associated with each other.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using mixer neural networks. One of the methods includes obtaining one or more images comprising a plurality of pixels; determining, for each image of the one or more images, a plurality of image patches of the image, wherein each image patch comprises a different subset of the pixels of the image; processing, for each image of the one or more images, the corresponding plurality of image patches to generate an input sequence comprising a respective input element at each of a plurality of input positions, wherein a plurality of the input elements correspond to respective different image patches; and processing the input sequences using a neural network to generate a network output that characterizes the one or more images, wherein the neural network comprises one or more mixer neural network layers.

A method for meeting quality of service (QoS) requirements in a flash controller that includes one or more instruction queues and a neural network engine. A configuration file for a QoS neural network is loaded into the neural network engine. A current command is received at the instruction queue(s). Feature values corresponding to commands in the instruction queue(s) are identified and are loaded into the neural network engine. A neural network operation of the QoS neural network is performed using as input the identified feature values to predict latency of the current command. The predicted latency is compared to a first latency threshold. When the predicted latency exceeds the first latency threshold one or more of the commands in the instruction queue(s) are modified. The commands are not modified when the predicted latency does not exceed the latency threshold. A next command in the instruction queue(s) is then performed.

A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.

Provided are a device and method for assessing a user's learning ability. The method includes acquiring target assessment data of a target user and a reference user related to a target domain, wherein the target assessment data includes question data related to the target domain and answer data of each of the target user and the reference user for the question data, acquiring a target neural network model of which training has been completed, acquiring comparison information representing the target user's ability in relation to the reference user's ability in the target domain through the target neural network model, and calculating the target user's virtual score in the target domain on the basis of the comparison information.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

The disclosed systems and methods provide a novel framework that provides mechanisms for a Deep & Cross Network (DCN) framework that performs distilled deep prediction for personalized stream ranking on portal websites. The disclosed framework is scalable to satisfy the much more stringent latency and computational requirements required by current network operating environments. The disclosed framework is able to dynamically evaluate and leverage live traffic on network sites in order to provide, update and maintain current recommendations for users as they traverse to a portal and when they navigate within the portal. The disclosed framework implements a DCN model(s) that is capable of being compressed into a model size for a unified optimization within a live traffic environment by combining knowledge distillation and model compression techniques. The disclosed framework is built as a light-weight deep learning model that can be served in production and perform on par with large models.

A system and method to determine a retention prediction for a caregiver is disclosed. The system includes a database of caregiver data and patient data. The set of caregiver data and patient data are normalized to create a modified set of caregiver and patient data. The modified set of caregiver and patient data defines a set of parameters or inputs from the set of caregiver and patient data and a corresponding employment status. An analysis is performed of parameters correlated with an employment status for each of the caregivers. Based on the correlation and the modified set of caregiver and patient data, a training set of caregiver data is generated that includes at least one parameter that correlates with employment status. The machine learning model is trained using the training set. The training allows a prediction of an employment status associated with the parameter. The accuracy of the trained machine learning model is evaluated.

The invention discloses a method of photovoltaic cell parameter identification based on the improved equilibrium optimizer algorithm, which comprises: step 1, establishing PV cell model and fitness function; step 2, based on the measured output I-V data, predicting output data of PV cell by a BP neural network; step 3, identifying PV cell parameters by using IEO algorithm until convergence conditions of the IEO algorithm are reached, and finally outputting the optimal identified parameters. Solving technical problems of the existing technology such as, cannot reach the optimal parameter identification, and being easy to be trapped into the local optimal.