Visa International Service Association

Junpeng Wang

Wei Zhang

Liang Wang

Hao Yang

Michael Yeh

Yan Zheng

Liang Gou

Zhongfang Zhuang

Prince Osei Aboagye

Chiranjeet Chetia

Minghua Xu

Shi Cao

Harishkumar Sundarji Majithiya

Roshni Ann Samuel

Shubham Jain

Haoyu Li

Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.

Method, system, and computer program product for normalizing embeddings for cross-embedding alignment

Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.

System, method, and computer program product for feature analysis using an embedding tree

Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.

Method, system, and computer program product for detecting group activities in a network

Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

System, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks

A method, system, and computer program product is provided for embedding compression and reconstruction. The method includes receiving embedding vector data comprising a plurality of embedding vectors. A beta-variational autoencoder is trained based on the embedding vector data and a loss equation. The method includes determining a respective entropy of a respective mean and a respective variance of each respective dimension of a plurality of dimensions. A first subset of the plurality of dimensions is determined based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. A second subset of the plurality of dimensions is discarded based on the respective entropy of the respective mean and the respective variance for each respective dimension of the plurality of dimensions. The method includes generating a compressed representation of the embedding vector data based on the first subset of dimensions.

Method, system, and computer program product for embedding compression and regularization

A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

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Columbus, OH, United States of America

Junpeng Wang