The presently disclosed subject matter includes a computerized method and system that provide the ability to train and execute a unique machine learning (ML) model specifically configured to enhance classifier (e.g., RegEx) output by identifying and removing false positive results from the classifiers output. Classifier output, comprising a collection of data-subsets (e.g., columns in a relational database) of one or more structured or semi-structured data sources (e.g., tables of a relational database), are transformed to be represented by a plurality of numerical vectors. The numerical vectors are used during a training phase (as well as the execution phase) for training a machine learning model to enhance the classifier output and reduce false positives.

Provided are a latency prediction method and a computing device for the same. The latency prediction method includes receiving a deep learning model and predicting on-device latency of the received deep learning model using a latency predictor which is trained on the basis of a latency lookup table. The latency lookup table includes information on single neural network layers and latency information of the single neural network layers on an edge device.

A method of generating a pre-training model, an electronic device and a storage medium, which relate to a field of an artificial intelligence technology, in particular to a computer vision and deep learning technology. The method includes: determining a performance index set corresponding to a candidate model structure set, the candidate model structure set is determined from a plurality of model structures included in a search space, and the search space is a super-network-based search space; determining, from the candidate model structure set, a target model structure corresponding to each chip according to the performance index set, each target model structure is a model structure meeting a performance index condition; and determining, for each chip, the target model structure corresponding to the chip as a pre-training model corresponding to the chip, the chip is configured to run the pre-training model corresponding to the chip.

Methods and system for privacy preserving information exchange in a network of electronic devices are disclosed. In one embodiment, a method is implemented in an electronic device to serve as a local party for information exchange between the local party and another electronic device to serve as an aggregator. The method includes storing a plurality of values in a 2D vector, where a first dimension of the 2D vector is based on the number of values, and where each position in the first dimension has one unique value. The method further includes transmitting the 2D vector to the aggregator with masking for the aggregator to prevent the aggregator from decoding the 2D vector, where aggregating the masked 2D vector with masked 2D vectors from other local parties allows decoding of the aggregated 2D vector.

A method for capturing user workflows can include tracking user queries for a plurality of users, correlating the user queries between two or more users of the plurality of users, determining that the user queries of the two or more users of the plurality of users are correlated, and classifying the user queries of the at least two users as a workflow neighbor. The workflow neighbor defines a set of time series data or features.

A method for capturing user workflows can include tracking user queries for a plurality of users, correlating the user queries between two or more users of the plurality of users, determining that the user queries of the two or more users of the plurality of users are correlated, and classifying the user queries of the at least two users as a workflow neighbor. The workflow neighbor defines a set of time series data or features.

This disclosure relates to recommendations made to users based on learned behavior patterns. User behavior data is collected and grouped according labels. The grouped user behavior data is labeled and used to train a machine learning model based on features and tasks associated with the classification. User behavior is then predicted by applying the trained machine learning model to the collected user behavior data, and a task is recommended to the user.

This disclosure relates to recommendations made to users based on learned behavior patterns. User behavior data is collected and grouped according labels. The grouped user behavior data is labeled and used to train a machine learning model based on features and tasks associated with the classification. User behavior is then predicted by applying the trained machine learning model to the collected user behavior data, and a task is recommended to the user.

An information processing device includes an acquisition unit (551) that acquires information related to a communication environment, and a determination unit (552) that determines a mode to be used on the basis of the information related to the communication environment among a first mode of determining a communication parameter on the basis of a measurement result using a reference signal, a second mode of determining the communication parameter on the basis of a learning result of machine learning using known information related to communication, and a third mode of determining the communication parameter according to the first mode and/or the second mode.

Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.