Google Inc.

Elad Edwin Tzvi Eban

Yair Movshovitz-Attias

Xiyang Luo

Alan Mackey

Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.

Constrained classification and ranking via quantiles

Systems and methods described herein may relate to training an artificial neural network (ANN) using a differentiable Jaccard Loss approximation. An example embodiment may involve obtaining a training image and a corresponding ground truth mask that represents a desired segmentation of the training image. The embodiment may further involve applying an ANN on the training image to generate an output segmentation of the training image that depends on a plurality of weights of the ANN and determining a differentiable Jaccard Loss approximation based on the output segmentation of the training image and the ground truth mask. The embodiment also involves modifying one or more weights of the ANN based on the differentiable Jaccard Loss approximation and providing a representation of the ANN as modified to a mobile computing device.

Differentiable jaccard loss approximation for training an artificial neural network

Audio recorded by a cellphone or other portable recording device (e.g., audio recorded as part of a video recording of a play or other event) is often of low quality, due to the limitations of the portable recording device. Multiple audio recordings, made during the same period of time and near the same location, can be combined to generate an improved-quality audio recording of an event. The audio recordings may be accessible to a server that selects the audio recordings and performs the combination. To protect the privacy of persons whose audio is used, more than a minimum number of recordings could be combined and/or no more than a threshold amount of any one recording could be used to generate a combined recording. Additionally, a provided 'clean' recording could include more than a threshold amount of the audio provided by a user or device that requests such a 'clean' recording.

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Sunnyvale, CA, United States of America

Elad Edwin Tzvi Eban