A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

A computer-implemented method for performing a classification of an input signal by a neural network includes: computing, by a feature extraction unit of the neural network, a D-dimensional query vector, wherein D is an integer; generating, by a classification unit of the neural network, a set of C fixed D-dimensional quasi-orthogonal bipolar vectors as a fixed classification matrix, wherein C is an integer corresponding to a number of classes of the classification unit; and performing a classification of a query vector based, at least in part, on the fixed classification matrix.

A computer-implemented method for performing a classification of an input signal by a neural network includes: computing, by a feature extraction unit of the neural network, a D-dimensional query vector, wherein D is an integer; generating, by a classification unit of the neural network, a set of C fixed D-dimensional quasi-orthogonal bipolar vectors as a fixed classification matrix, wherein C is an integer corresponding to a number of classes of the classification unit; and performing a classification of a query vector based, at least in part, on the fixed classification matrix.

The present disclosure provides a method for generating a recommendation model, a content recommendation method, and a content recommendation apparatus, and an electronic device, and relates to an artificial intelligence field and a deep learning field. The method for generating a recommendation model includes: obtaining a graph training sample set; inputting the graph training sample set into a machine learning model to train the machine learning model, in which the machine learning model includes at least one low-rank graph convolutional network, and the low-rank graph convolutional network includes a complete weight matrix composed of a first low-rank matrix and a second low-rank matrix; in which a training objective of the low-rank graph convolutional network includes a first parameter item, a second parameter item and a non-convex low-rank item; and in responding to detecting that a training end condition is met, determining the machine learning model as a recommendation model.

Dual machine translators are trained by generating a translated medical image by operation of an illustrative model on an original medical record, generating information based on whether the translated medical image is natural in a modality of medical imaging, producing a back-translated medical record by operation of an interpretive model on the translated medical image, calculating a reward by comparing the back-translated medical record to the original medical record, updating parameters of the illustrative model in response to the information and the reward, and updating parameters of the interpretive model in response to the reward.