Google Inc.

Ankit Singh Rawat

Sanjiv Kumar

Xinnan Yu

Ananda Theertha Suresh

Sashank Jakkam Reddi

Jiecao Chen

Aditya Krishna Menon

Systems and methods for low bias negative sampling of classes according to the sampled softmax method are described herein. The systems and methods can include training a machine-learned model for classifying inputs into one or more classes of a plurality of classes, each of the plurality of classes having an associated class embedding in a plurality of class embeddings. The systems and methods can include selecting, by the one or more computing devices, one or more negative classes from the plurality of classes based at least in part on a probability distribution approximating a softmax distribution, wherein the probability distribution is determined based at least in part on a Random Fourier Features map.

Sampled softmax with Random Fourier features

A method for training a machine learning model, e.g., a neural network, using a regularization scheme is disclosed. The method includes generating regularized partial gradients of losses computed using an objective function for training the machine learning model.

Training machine learning models to be robust against label noise

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New York, NY, United States of America

Ankit Singh Rawat