Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for post-hoc deferral for classification tasks. In particular, a system can perform either post-hoc threshold correction or post-hoc rejector training to account for the cost of deferring model inputs to an expert system for classification.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for accounting for long-tail training data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for multi-stage computationally-efficient inference using a first and second neural network.

Abstract: Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.

Abstract: Generally, the present disclosure provides systems and methods for performing machine learning in hyperbolic space. Specifically, techniques are provided which enable the learning of a classifier (e.g., large-margin classifier) for data defined within a hyperbolic space (e.g., which may be particularly beneficial for data that possesses a hierarchical structure).

Abstract: 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.