Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-sensitive training of a neural network. In one aspect, a system comprises a central memory configured to store current values of a set of neural network parameters and one or more computers that are configured to implement a plurality of worker computing units, where each worker computing unit is configured to repeatedly perform operations comprising obtaining current values of the set of neural network parameters from the central memory, sampling a batch of network inputs from a set of training data, determining a respective gradient corresponding to each network input, determining an aggregated gradient based on the gradients, identifying a subset of a set of gradient values as target values, generating a noisy gradient by combining random noise with the target gradient values, and updating the current values of the set of neural network parameters.

Abstract: Implementations disclosed herein are directed to various techniques for mitigating and/or preventing catastrophic forgetting in federated learning of global machine learning (ML) models. Implementations may identify a global ML model that is initially trained at a remote server based on a server data set, determine server-based data for global weight(s) of the global ML model, and transmit the global ML model and the server-based data to a plurality of client devices. The server-based data may include, for example, EWC loss term(s), client augmenting gradients, server augmenting gradients, and/or server-based data. Further, the plurality client devices may generate, based on processing corresponding predicted output and using the global ML model, and based on the server-based data, a corresponding client gradient, and transmit the corresponding client gradient to the remote server. Implementations may further generate an updated global ML model based on at least the corresponding client gradients.

Abstract: Provided are protease inhibitor compounds that find use in treating or preventing coronavirus disease. In some embodiments, the coronavirus disease is COVID-19. Also provided are compositions and kits comprising the compounds, as well methods of using the compounds to treat or prevent coronavirus disease. Methods of assessing inhibition of coronavirus protease activity by an agent are also provided.

Abstract: An exemplary clustering and computation reuse method comprises providing an artificial convolutional neural network; detecting that neuron vectors associated with an input layer and/or a hidden layer of the convolutional neural network are similar to one another; detecting similarities among the neuron vectors associated with the input layer and/or the at least one hidden layer during execution of a computer program; clustering similar neuron vectors into groups; computing a centroid vector for each group; performing, by a computer processor, computations using the centroid vector associated with one of the groups as a representative for one of the members of the group to generate an output for the computation, wherein the output is generated during execution of the computer program; and reusing, by the computer processor, the output for the computation involving the centroid vector for another computation involving another member of the group.