Visa International Service Association

Javid Ebrahimi

Wei Zhang

Liang Wang

Xiaobo Dong

Minje Choi

Provided are systems for generating a machine learning model for classification tasks using unadversarial training that include a processor to perform an unadversarial training procedure to train a machine learning model to provide a trained machine learning model. When performing the unadversarial training procedure, the processor is programmed or configured to receive a training dataset including a plurality of training samples; generate a noise vector for the plurality of training samples based on a uniform distribution; perturb each training sample of the plurality of training samples; obtain a gradient; generate an updated noise vector based on the gradient; perturb each training sample of the plurality of training samples based on the updated noise vector; and update a model weight of the machine learning model based on the second plurality of perturbed training samples to provide the trained machine learning model. Methods and computer program products are also provided.

System, method, and computer program product for reducing dataset biases in natural language inference tasks using unadversarial training

Methods and systems are described. A method includes accessing transaction data related to restaurants associated with a plurality of geographically separate locations, determining a number of co-visitors shared by each of the restaurants associated with the plurality of geographically separate locations above a predetermined threshold, generating a graphical representation of the plurality of restaurants based on the number of the co-visitors shared by restaurants with the co-visitors above the predetermined threshold and the distance between the restaurants with the co-visitors. The graphical representation is transformed into restaurant embeddings and a neural network model is used to generate restaurant preferences based on the restaurant embeddings.

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Mountain View, CA, United States of America

Javid Ebrahimi