Google Inc.

Cho-Jui Hsieh

Sanjiv Kumar

Yang Li

Chih-Jen Lin

Michael Ringgaard

Si Si

Pei-Hung Chen

Yin-Wen Chang

Kai-Wei Chang

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for decreasing neural network inference times using softmax approximation. One of the methods includes maintaining data specifying a respective softmax weight vector for each output in a vocabulary of possible neural network outputs; receiving a neural network input; processing the neural network input using one or more initial neural network layers to generate a context vector for the neural network input; and generating an approximate score distribution over the vocabulary of possible neural network outputs for the neural network input, comprising: processing the context vector using a screening model configured to predict a proper subset of the vocabulary for the context input; and generating a respective logit for each output that is in the proper subset, comprising applying the softmax weight vector for the output to the context vector.

Decreasing neural network inference times using softmax approximation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for polynomial mapping of data for linear SVMs. In one aspect, a method includes training a linear classifier by receiving feature vectors and generating a condensed representation of a mapped vector corresponding to a polynomial mapping of each feature vector, the condensed representation including an index into a weight vector for each non-zero component of the mapped vector. A linear classifier is trained on the condensed representations. In another aspect, a method includes receiving a feature vector, identifying non-zero components resulting from a polynomial mapping of the feature vector, and mapping the combination of one or more elements of each non-zero component to a weight in a weight vector to determine a set of weights. The feature vector is classified according to a classification score derived by summing the set of weights.

Efficient polynomial mapping of data for use with linear support vector machines

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San Jose, CA, United States of America

Cho-Jui Hsieh