Unidimensional embedding using multi-modal deep learning models

A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to:

• receive, by an autoencoder, transaction data for a plurality of transactions, the transaction data comprising a plurality of fields, the plurality of fields comprising a plurality of data types, the plurality of data types comprising different data types;
• generate, by an embeddings layer of the autoencoder, an embedding vector for a first transaction of the plurality of transactions, the embedding vector comprising floating point values to represent the plurality of data types;
• generate, by one or more fully connected layers of the autoencoder based on the embedding vector, a plurality of statistical distributions for the first transaction, each statistical distribution comprising a respective embedding vector;
• sample, by a sampling layer of the autoencoder, a first statistical distribution of the plurality of statistical distributions;
• decode, by a decoder of the autoencoder, the first statistical distribution to generate an output representing the first transaction;
• and
• store the output in a storage medium.

A computing apparatus comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, configure the processor to:
• receive, by an autoencoder executing on the processor, transaction data for a plurality of transactions, the transaction data comprising a plurality of fields, the plurality of fields comprising a plurality of data types, the plurality of data types comprising different data types;
• generate, by an embeddings layer of the autoencoder, an embedding vector for a first transaction of the plurality of transactions, the embedding vector comprising floating point values to represent the plurality of data types;
• generate, by one or more fully connected layers of the autoencoder based on the embedding vector, a plurality of statistical distributions for the first transaction, each statistical distribution comprising a respective embedding vector;
• sample, by a sampling layer of the autoencoder, a first statistical distribution of the plurality of statistical distributions;
• decode, by a decoder of the autoencoder, the first statistical distribution to generate an output representing the first transaction;
• and
• store the output in a storage medium.