Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a machine learning task on a network input to generate a network output. One of the systems comprises an attention neural network configured to perform the machine learning task, the attention neural network comprising a plurality of attention layers, each attention layer comprising an attention sub-layer that is arranged in parallel with a feed-forward sub-layer.

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: A system including a main neural network for performing one or more machine learning tasks on a network input to generate one or more network outputs. The main neural network includes a Mixture of Experts (MoE) subnetwork that includes a plurality of expert neural networks and a gating subsystem. The gating subsystem is configured to: apply a softmax function to a set of gating parameters having learned values to generate a respective softmax score for each of one or more of the plurality of expert neural networks; determine a respective weight for each of the one or more of the plurality of expert neural networks; select a proper subset of the plurality of expert neural networks; and combine the respective expert outputs generated by the one or more expert neural networks in the proper subset to generate one or more MoE outputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more sparse attention layers.

Abstract: A system and method are disclosed for providing a real-time wireless video surveillance system. The video surveillance system leverages edge computing to enable wireless video surveillance distributing video processing between edges of the network and the cloud to reduce the amount of video that is uploaded to the cloud for analysis.

Abstract: A system and method are disclosed for providing a real-time wireless video surveillance system. The video surveillance system leverages edge computing to enable wireless video surveillance distributing video processing between edges of the network and the cloud to reduce the amount of video that is uploaded to the cloud for analysis.

Abstract: A method for monitoring radio frequency (RF) transmitters in an environment, that fits a probability mixture model (PMM) comprising a plurality of probability density functions (PDFs) at least two of which are of a different type, to RF power measurements of RF signals received in the environment to determine a number and characteristics of RF transmitters operating in the environment.

Abstract: A system and method are disclosed for providing a real-time wireless video surveillance system. The video surveillance system leverages edge computing to enable wireless video surveillance distributing video processing between edges of the network and the cloud to reduce the amount of video that is uploaded to the cloud for analysis.

Abstract: A method for monitoring radio frequency (RF) transmitters in an environment, that fits a probability mixture model (PMM) comprising a plurality of probability density functions (PDFs) at least two of which are of a different type, to RF power measurements of RF signals received in the environment to determine a number and characteristics of RF transmitters operating in the environment.