Abstract: A method can include generating, using a language model, an intermediate representation of an activity history of a user account based on actions performed with respect to a browser; generating, using the language model, a first textual summary for a first intent based on the intermediate representation; generating, using the language model, a second textual summary of a second intent based on the intermediate representation; associating the first textual summary and the second textual summary with the user account; selecting, from the first textual summary and the second textual summary, a selected summary based on a requested intent; and providing content based on the selected summary.

Abstract: Provided are systems and methods for performing self-supervised learning (SSL) of user sequence representations. In particular, an example method can include obtaining sequences of user feature data, applying various augmentation techniques such as random masking or permutation, and then processing these through a user sequence model to generate embeddings. These embeddings can be further transformed by a projection network, and a correlation-based loss function, such as the Barlow Twins loss, can be used to refine the model parameters.

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: It is an object of the disclosed technique to provide a novel method and system for shared concurrent access to a memory cell. In accordance with the disclosed technique, there is thus provided a system for shared concurrent access to a memory cell, which includes at least one shared memory cell, an evaluator and a plurality of processing agents coupled to the input of the evaluator. The evaluator is further coupled with the at least one memory cell. The evaluator is configured to evaluate an expression for performing multiple concurrent composite assignments on the at least one shared memory cell. The evaluator further allows each of the plurality of processing agents to perform concurrent composite assignments on the at least one shared memory cell. The composite assignments do not include a read operation of the at least one shared memory cell by the plurality of processing agents.

Abstract: It is an object of the disclosed technique to provide a novel method and system for shared concurrent access to a memory cell. In accordance with the disclosed technique, there is thus provided a system for shared concurrent access to a memory cell, which includes at least one shared memory cell, an evaluator and a plurality of processing agents coupled to the input of the evaluator. The evaluator is further coupled with the at least one memory cell. The evaluator is configured to evaluate an expression for performing multiple concurrent composite assignments on the at least one shared memory cell. The evaluator further allows each of the plurality of processing agents to perform concurrent composite assignments on the at least one shared memory cell. The composite assignments do not include a read operation of the at least one shared memory cell by the plurality of processing agents.